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Introduction to Matlab fit

MATLAB fit method can be used to fit a curve or a surface to a data set. Fitting a curve to data is a common technique used in Artificial intelligence and Machine learning models to predict the values of various attributes.

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For example, if we compare the weight of an item like rice with its price; ideally, it should increase linearly (Price will increase as the weight of rice will increase). If we fit a curve to this data of weight and price, we will get mostly a linear curve. Now someone looking at this linear curve can easily interpret the relation between the 2 attributes (weight and price in our example), without looking at the data.

Syntax:

fitobject = fit (a, b, fitType) is used to fit a curve to the data represented by the attributes ‘a’ and ‘b’. The type of model or curve to be fit is given by the argument ‘fitType’

Various values which the argument ‘fitType’ can take are given in the table below:

Model Name Description

‘poly1’

A polynomial curve with linear nature

‘poly11’

A polynomial surface with linear nature

‘poly2’

A polynomial curve with quadratic nature

‘linearinterp’

Linear piecewise interpolation

‘cubicinterp’

Cubic piecewise interpolation

‘smoothingspline’

A curve of the nature smoothing spline

Table 1

Let us now understand how to fit a curve or a surface to data in MATLAB:

We will need some data to which we will fit the curve, for our examples, we will use some inbuilt data sets provided by MATLAB like ‘carsmall’ and ‘census’.

Examples of Matlab fit

Let us discuss examples of Matlab fit.

Example #1

In this example, we will use the ‘carsmall’ data provided by MATLAB. The data is of various attributes of cars manufactured over the years 1970, 1976, and 1982. It has attributes like ‘Acceleration’, ‘Cylinders’, ‘Horsepower’ etc. which represent various features of a car. We will load this data to our workspace and will fit a curve to its attributes ‘Acceleration’ and ‘Displacement’. The steps to be followed for this example are:

Load the ‘carsmall’ data to the workspace

View the file loaded above to understand its attributes

Use the ‘fit’ function to fit a curve to the loaded data

Plot the model created in above step

1. load carsmall

[Using the ‘load’ command to load the ‘carsmall’ data set to our workspace]

[Using the ‘load’ command to load the ‘carsmall’ data set to our workspace]

2. whos -file carsmall

[Using ‘whos’ command to view the file loaded above]

[Using ‘whos’ command to view the file loaded above]

3. C = fit(Acceleration, Displacement, ‘poly2’)

[Using the ‘fit’ command to fit a curve to the data. The first 2 parameters represent the attributes to which we want to fit the curve and the 3rd parameter represents the type of curve which we want to fit (please refer to Table 1 for this)]

[Using the ‘fit’ command to fit a curve to the data. The first 2 parameters represent the attributes to which we want to fit the curve and the 3parameter represents the type of curve which we want to fit (please refer to Table 1 for this)]

4. plot(C, Acceleration, Displacement)

[Using ‘plot’ command to plot the model created in above step]

[Using ‘plot’ command to plot the model created in above step]

This is how our input and output will look like in MATLAB command window:

Input 1:

Loading the carsmall data set:

Input 2:

Fitting the curve to the data:

Input 3:

Plotting the model created above:

Output 1:

Output 2:

Output 3:

As we can see in Output 3, we have obtained a curve that fits our data. Output 1 and Output 2 represent the data attributes and the model respectively.

In the same example, we can also fit a different type of curve as per our requirement. Let us try to fit ‘smoothingspline’ curve to the above data.

The code will be similar as in the above example with a change in line 3

Code:

 1. load carsmall

[Using the ‘load’ command to load the ‘carsmall’ file to our workspace]

[Using the ‘load’ command to load the ‘carsmall’ file to our workspace]

2. whos -file carsmall

[Using ‘whos’ command to view the file loaded above]

[Using ‘whos’ command to view the file loaded above]

 3. C = fit(Acceleration, Displacement, ‘smoothingspline’)

[Please note that the 3rd argument is now ‘smoothingspline’]

[Please note that the 3argument is now ‘smoothingspline’]

4. plot(C, Acceleration, Displacement)

[Using ‘plot’ command to plot the model created in above step]

Input 1:

Loading the carsmall data set:

Input 2:

Fitting the curve to the data:

Input 3:

Plotting the model created above:

Output 1:

Output 2:

Output 3:

As we can see in Output 3, we have obtained a smoothing spline curve that fits our data.

Example #2

In this example, we will use the ‘census’ data provided by MATLAB. The data is of the US and gives the population of the country in a particular year. It has 2 attributes ‘cdate’ and ‘pop’ representing ‘census date’ and ‘population’. We will load this data to our workspace and will fit a curve to it. The steps to be followed for this example are:

Load the census data to the workspace

View the file loaded above to understand its attributes

Use the ‘fit’ function to fit a curve to the loaded data

Plot the model created in above step

Code:

load census

[Using the ‘load’ command to load the census file to our workspace]

[Using ‘whos’ command to view the file loaded above]

[Using ‘whos’ command to view the file loaded above]

C = fit(cdate, pop, 'poly2')

[Using the ‘fit’ command to fit a curve to the data. The first 2 parameters represent the attributes to which we want to fit the curve and the 3rd parameter represents the type of curve which we want to fit (please refer to Table 1 for this)]

[Using the ‘fit’ command to fit a curve to the data. The first 2 parameters represent the attributes to which we want to fit the curve and the 3parameter represents the type of curve which we want to fit (please refer to Table 1 for this)]

plot(C, cdate, pop)

[Using ‘plot’ command to plot the model created in above step]

[Using ‘plot’ command to plot the model created in above step]

This is how our input and output will look like in MATLAB command window:

Input 1:

Loading the census data set:

Input 2:

Fitting the curve to the data:

Input 3:

Plotting the model created above:

Output 1:

Output 2:

Output 3:

As we can see in Output 3, we have obtained a curve that fits our data. Output 1 and Output 2 represent the data attributes and the model respectively.

Conclusion

We use ‘fit’ function in MATLAB to fit a curve to our data set

Fitting a curve is very useful technique used in Machine learning

We can control the type of curve that we want to fit to our data by using the ‘fitType’ argument.

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Learn The Different Examples Of Plsql Pivot

Introduction to PLSQL pivot

PL/SQL provides the different types of functionality to the user; the pivot is the one type of functionality that is provided by the PL/SQL. Basically, we call it a pivot clause. By using pivot clauses, we cross table query as per requirement; in another way, we can combine, or we can aggregate our result from rows into the columns as per our requirement. Basically, the pivot clause introduced in Oracle 11g and the pivot returns more than one column after the execution. By using pivot clauses, we can combine the difference into a single result and generate the required output.

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Syntax select * from (select colm name 1, colm name name N from specified table where conditional expression) PIVOT

Explanation

First, we use the select clause to retrieve the records from the specified table. Inside the select, we write the subquery with a column name, and here we specify where clause with conditional expression.

Specified aggregate function name: it is used to specify the aggregate function name that we can write any function name such as SUM, MIN, MAX, etc.

IN (specified expression 1, specified expression 2,…..specified expression N): It is used to hold the list of column name 2 that values to pivot into the cross table.

Specified subquery: Basically, it is used instead of list values, and the output of the subquery would be utilized to calculate the values for column 2 in the cross-tabulation query output, which would then be translated to headings in this syntax.

How does pivot work in PL/SQL?

Now let’s see how pivot clauses work in PL/SQL as follows.

Let’s see the different ways to implement the pivot clause as follows.

Specify the Group Columns:

Any columns not stated in the FOR clause are utilized as a part of the Oracle PIVOT GROUP BY when employing the PIVOT keyword. The only column in the previous example was the location column, which was fine.

We can also use the Where clause with pivot clause:

The results of our searches above provide a pivoted summary of all data. A few fields are used to aggregate all of the entries, and the SUM of the selling amount is displayed.

What if you want to limit it to just a few rows?

Like a regular SELECT query, you may use a WHERE clause. Then we will get the error due to incorrect syntax, so we need to write the correct syntax that means the PIVOT clause must appear after the WHERE clause; this is the case.

Now let’s see how we can use Aliasing in the pivot column:

The column headings will be shown as the table’s column name in the queries we’ve looked at so far. What if you want to call them something else? A column alias can be specified using the PIVOT keyword. Both the pivot clause and the pivot in a clause can be used for this.

Now let’s see how we can perform the multiple aggregations in pivot:

We can make the group of multiple columns:

This is another way to implement the multiple columns into the pivot statement to group by multiple columns as per our requirement.

We can implement pivot with XML as follows:

You may display your findings in an XML format using the PIVOT keyword. It’s as simple as following the PIVOT keyword with the XML keyword.

Examples of PLSQL pivot

Now let’s see the different examples of pivot clauses in PL/SQL for better understanding as follows.

First, we need to create the table by using the following create table statement as follows.

create table stud(roll_no integer not null, name varchar2(50), dept_id integer not null, primary key(roll_no));

Explanation

By using create table statement, we created a new table name as a stud with different attributes such as roll_no, name, and dept_id with different data types, and in this example, the primary key is roll_no. The final output of the above statement we illustrated by using the following screenshot as follows.

insert into stud(roll_no, name, dept_id) values(1,'Jenny',10); insert into stud(roll_no, name, dept_id) values(2,'Jenny',10); insert into stud(roll_no, name, dept_id) values(3,'Jenny',20); insert into stud(roll_no, name, dept_id) values(5,'Sameer',20); insert into stud(roll_no, name, dept_id) values(6,'Sameer',10); insert into stud(roll_no, name, dept_id) values(7,'Sameer',20);

Explanation

In the stud table, we inserted a total of 6 records by using the above statement. The final output of the above statement we illustrated by using the following screenshot as follows.

If records are not in order, then we can use order by clause to make the records in order. In this example, all records we order by roll_no, as shown in the above screenshot.

Now implement the pivot clause that means write the cross table subquery as follows.

select * from (select name, dept_id from stud) pivot (count(dept_id) for dept_id IN (10, 20, 30)) order by name;

Explanation

In the above example, we write the two different queries that we call subquery and merge by using the pivot clause. Now let’s see how it works. In the above example, we first decide which field we want to add in the pivot clause; here, we add name and dept_no. After that, we need to specify the column in any order that we want. The next part of the query, it contains the aggregate function and pivot value that we want, as shown in the above statement. The final output of the above statement we illustrated by using the following screenshot as follows.

Conclusion – PLSQL pivot

We hope from this article you learn PL/SQL pivot. From the above article, we have learned the basic syntax of the pivot, and we also see different examples of the pivot. From this article, we learned how and when we use PL/SQL pivot.

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Grep Command In Linux: Syntax, Options, Examples, & More

As a system admin on a Linux system, you might have to parse through a huge log file in Linux. It might seem a painstaking task, especially in the instances where you have to match patterns. Thankfully, grep command in Linux is a boon for such situations. If you are wondering what is grep command and how it works, below, we have prepared an easy guide to help you understand this useful Linux command.

What Is the grep Command in Linux

The grep command is a powerful command line tool in Linux used to search and filter out specific patterns or strings in a file, directory, or even in the output of other commands. You may be wondering about its unusual name; well it stands for “Global Regular Expression Print.” It was first introduced by Ken Thompson in 1973 for the Unix operating system. With its versatility and ease of use, the grep command is a must-have tool in every Linux user’s arsenal.

Generally, the grep command comes preinstalled on most Linux distros, but if you find it to be missing on your system, install it using the following commands:

Install on Debian-based systems:

sudo apt install grep

Install on Fedora-based systems:

Install on Arch-based systems:

sudo pacman -S grep

Linux Grep command: Syntax & Options

Using the grep command in Linux is pretty straightforward, thanks to its simple syntax along with the multiple options to play with. The syntax to use the grep command is:

How to Use the Grep Command in Linux

Say, for example, you want to match email addresses, you can use the regex “(.+)@(.+)n“. Seems complicated? Let’s break down this:

(.+) matches any characters except new lines

@ checks if the “@” symbol is present in the given sentence.

Now that you know what are regular expressions and how the grep command works, let’s now see how to use the grep command in Linux.

Search For Strings in Files with grep Command

The most common way to use grep is to search for specific strings in a file. The basic syntax to search with the grep command in files is:

grep "mango" fruits.txt

If you want to search while ignoring the case, then use the -i flag with the grep command for the above example:

grep -i "mango" fruits.txt

Search in Multiple Files with grep

With the grep command, you can not only search in a single file but also multiple files. This feature particularly becomes very handy when you need to go through multiple large files. The syntax to search for strings/patterns in multiple files with the grep command is:

For example, if you want to search for the string “student”, inside the files “student1.txt” and “student2.txt,” use this command:

grep "John" chúng tôi student2.txt

Search All Files in a Directory with grep

Suppose, you need to search for a string and you don’t remember the file name where it exists. You can obviously write all the filenames present in the directory, but instead, you can use simply use the “*” wildcard as shown:

grep "apple" *

If you want to search in specific file types only, use this syntax:

For example, if you want to search for the word “mango” only in .txt files, use this command:

Using Regular Expression with grep

For example, if you want to filter out email IDs use this command:

grep -e "(.+)@(.+)" emails.txt

Search for Multiple Keywords using grep Command

Count Matching Results using grep Command

Sometimes, you may need to know the number of matching results. For this, you can use the -c flag with the grep command:

For example, if you need to see how many email IDs are there in the file student1.txt, use this command:

grep -E -c "(.+)@(.+)" student1.txt

If you want to see the number of lines not matching the search query, simply add the -v flag along with -c:

grep -v -E -c "(.+)@(.+)" student1.txt

Using grep Command with Shell Pipes

For example, if you want to see how many times the gcc command has been executed previously, you can use this command:

This will show only the results containing the word “gcc,” as shown in the picture below.

Using grep Command in Linux

The grep command is an essential tool for users who want to search for specific patterns or words in various files, directories, or even in other Linux command outputs. Once you have got hold of all the essential options and syntax, the grep command can help you to increase your productivity exponentially. While you are here, check out the new features in Ubuntu 23.04.

Grep Command in Linux FAQs

Explanation And Examples Of Accrued Interest

Definition of Accrued Interest

Accrued interest (Acc. Int) refers to the amount of interest that has been accrued on investments or borrowings but the same has not been yet. It is treated as financial gain or obligation depending upon whether it is accrued on the investments made or the amount borrowed from debentures or bonds. It is recorded in the accounts to follow the accrual accounting system.

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Explanation

As per the accrual accounting system, the income that is incurred but not yet received or expense that is incurred but not yet paid is to be recorded in the accounts to reflect the accurate and fair view for the period mentioned. Acc. Int is the interest received on the investment, like fixed deposits, loans given, interest on bonds, etc., whereas Acc. Int can also be a liability, like interest on the amount borrowed from debentures or bonds. It follows the guidelines of generally accepted accounting principles like revenue recognition and the matching accounting principle. It refers to the accumulated interest due for receipt of payment but not received or paid, as the case may be. It is recorded at the end of the accounting period, i.e., on the balance sheet date, to reflect the accurate and fair view of the accounting.

Formula for Acc. Int

For borrowings like debentures and bonds, the formula for Acc. Int is as under:

Accrued Interest = Borrowed Amount * Yearly Interest Rate / 365 * Period for Which Interest Is Accrued.

The formula for Acc. Int on investment

Accrued Interest = Investment Amount * Yearly Interest Rate / 365 * Period for Which Interest Is Accrued.

Acc. Int as on the last day of the financial year or as on the balance sheet date is calculated for the period it is due. For example, the interest for each quarter will be received on the 10th of the next quarter. So, the interest of the last quarter, which accrued on 31st March, will be accepted on 10th April. Hence Acc. Int for the previous quarter, i.e., Jan – March, is due and will be recorded in the accounts as Acc. Int.

Examples

Different examples are mentioned below:

Example #1

The company borrows $ 70,000 from the bank, and the annual interest rate is 5%. The amount was borrowed on 15-12-2024, where the interest payment is monthly. The financial year of the company closes on 31st December. Calculate the Acc. Int to be recognized in the books of accounts at the closing of the financial year?

Solution:

The period from 15th December to 31st December = 16 days

Accrued Interest = Borrowed Amount * Yearly Interest Rate / 365 * Period for Which Interest is Accrued

Accrued Interest = $70,000 * 5% / 365 * 16

Accrued Interest = $70,000 * 0.05 / 365 * 16

Accrued Interest = $153 (appx)

Particulars

Value

Borrowed Amount  $70,000

Yearly Interest rate 5%

Number of days in a year 365

Period for which interest is accrued 16

Accrued Interest  $153.42

Example #2

Company A Ltd deposits in fixed deposits amounting to $ 50,000. The interest rate on fixed deposits is 6%. A fixed deposit was made on 01-02-2024 and matured on 31-03-2024. The company closes the books on 31st March every year. Calculate the Acc. Int on the fixed deposit?

Solution:

Acc. Int is calculated as

Accrued Interest = Investment Amount * Yearly Interest Rate / 365 * Period for Which Interest is Accrued.

Acc. Int = $50,000 * 6% / 365 * 59 days

Acc. Int = $50,000 * 0.06 / 365 * 59

Acc. Int = $485 (Appx)

Particulars

Value

Borrowed Amount  $50,000

Yearly Interest rate 6%

Number of days in a year 365

Period for which interest is accrued 59

Acc. Int  $484.93

Accrued Interest on Bonds

A bond is a debt obligation for the borrower and is an asset for the lender. Hence the lender is entitled to receive the interest on bonds. The interest on bonds is generally known as coupons and is paid yearly, half-yearly, quarterly, or as decided at the bond issuance. The bond is a negotiable instrument, i.e., it can be transferred from one person to another person very quickly, but there is a problem concerning the interest at the time of sale, i.e., when the bond is transferred, the interest accrued can also be paid to the seller by the buyer of the bonds. So, interest is calculated at the time of sale of such a bond. For example, there is a bond in which interest accrued from April to September will be received in October by the buyer. The seller wants to sell the bond in July. Hence apart from the bond’s market price, the buyer also has to pay the interest for three months to the seller, which he will receive from the organization in October.

Accrued Interest vs Regular Interest

Acc. Int is the interest that gets due but has not been paid yet, whereas regular interest is the interest that is paid or received and recorded in the books of accounts.

Acc. Int is recognized in the accounts before the payment is made, whereas regular interest is recorded only after the receipt.

The payment or receipt cycle of Acc. Int is constant and decided before the investment or borrowing, and it cannot be changed. In contrast, the payment or receipt cycle of common interest is flexible and can be changed at any time per mutual decision.

Acc. Int is recorded per the accrual accounting system, i.e., on a due basis, whereas the regular interest is recorded on a receipt basis.

Conclusion

Acc. Int is the interest that gets due or accumulated but is not received or paid. Example of Acc. Int includes interest on fixed deposits, interest on bonds, interest on debentures, etc. The payment or receipt cycle of Acc. Int is pre-decided, and it is constant. Bond being the negotiable instrument, can be transferred at any time, so in that case, the seller is entitled to receive the interest apart from sale proceeds. Acc. Int is different from a regular interest in terms of flexibility as the receipt or payment cycle of regular interest is customized and changed with mutual consultation. Acc. Int is recorded as per the matching concept and as per the accrual system of accounting. The calculation of Acc. Int is to be done up to the last day of the financial year.

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Functional Preview And Examples Of Python Reduce

Introduction to Python Reduce

Reduce() function returns a map object. A function that is to be passed to all the elements of the list is applied in its arguments. Lambda functions are used to make the code more readable and other operator functions. In this topic, we are going to learn about Python Reduce.

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Syntax of Python Reduce reduce (function, iterable)

Function obligation to be executed for each item.

Iterable is a sequence, a collection. It returns an iterator object as many iterables as one desire can be sent.

Function consists of one parameter for each iterable.

Engineers today, are predominantly busy working and dealing with lists. Let’s say:

You work in Wall Street- you are analyzing lists of stock prices.

Writing software for a drone delivery service- you’re processing lists of orders.

You work at friendface- you’re profiling lists of users with your mountain of personal data.

A lot of code is spent analyzing, filtering, and combining the items in a list.

Map, Filter and Reduce functions are built-in high order functions in Python.

Often, using a generator expression over a reduced function is preferred. User preference is involved. Defining a function inline using lambda, the preferential expression is the generator and will be clean over the reduce function.

Functions usage in general – Python:

What are Lambda Expressions?

Lambda functions are quite useful when you require a short, throwaway anonymous function. Simplicity is that it can be used only once. Applied explicitly near the sorting and filtering of the data.

lambda arguments: expression

Just like functions, it is perfectly acceptable to have anonymous functions with no inputs.

Next, type a colon. Then finally you enter a single expression. This expression is a return value. Multi-line functionality or more than a single line is not possible using such functions.

A Functional Preview Of Reduce Data: a1, a2, a3,..,an Function: f reduce (f, data) :

Suppose you have a list/tuple /other iterable collection of data, (consider Data: a1, a2, a3…..,an. as it for time being)

Each piece of data is applied with the function: f.

With the reduce function(reduce (f, Data):), you first specify the function and then the data to iterate over.

The reduce function will iterate over the collection (f(a1), f(a2,),…., f(an)) of f applied to each piece of data.

The result is obtained by picking the first two elements of a sequence.

The previously attained result is applied with the same function and the number next to the second element, the result is cached.

Till container is left with no more elements, the process continues.

The console returns the end result.

Examples of Python Reduce

First consider importing the tool functools and reducing function from within it.

Now, for performing the multiplication, give a list of desired values.

Lambda function is used as discussed above, why and how it is used.

If you simply print the multiplier, it returns the reduce object.

We need to Convert the reduce object into a return result of which we are looking for.

from functools import reduce #Multiplication of all the numbers in the list data = [2, 3, 5, 7, 9, 11, 17, 19, 23, 29] multiplier = lambda x, y : x*y print(reduce (multiplier, data))

Output:

import functools def multiply(a,b): print("a=",a," b=",b) return a*b factorial=functools.reduce(multiply, range(1, 6)) print ('Factorial of 5 is: ', factorial)

Output:

3. Sum of given numbers is performed in the below example using reduce function.

from functools import reduce def do_sum(x1, x2): return x1 + x2 print(reduce(do_sum, [1, 2, 3, 4]))

Output: 

Conclusion

The map, filter and reduce functions greatly simplifies the process of working with lists and other iterable collections of data, In fact, if you use lambda expressions, your work can often be done in a single line. After you master these functions, you will realize Python should be a comedian because it is full of one-liners.

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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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