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Introduction to Python StopIteration

The following article outlines Python StopIteration as we know the topic ‘iterator’ and ‘iterable’ in Python. The basic idea of what the ‘iterator’ is? An iterator is an object that holds a value (generally a countable number) that is iterated upon. Iterator in Python uses the __next__() method to traverse to the next value. To tell that no more deals need to be traversed by the __next__() process, a StopIteration statement is used. Programmers usually write a terminating condition inside the __next__() method to stop it after reaching the specified state.

Syntax of Python StopIteration

When the method used for iterators and generators completes a specified number of iterations, it raises the StopIteration exception. It’s important to note that Python treats raising StopIteration as an exception rather than a mistake. Like how Python handles other exceptions, this exception can be handled by catching it. This active handling of the StopIteration exception allows for proper control and management of the iteration process, ensuring that the code can gracefully handle the termination of the iteration when required.

The general syntax of using StopIteration in if and else of next() method is as follows:

class classname: def __iter__(self): … … #set of statements return self; def __next__(self): if …. #condition till the loop needs to be executed …. #set of statements that needs to be performed till the traversing needs to be done return … else raise StopIteration #it will get raised when all the values of iterator are traversed How StopIteration works in Python?

It is raised by the method next() or __next__(), a built-in Python method to stop the iterations or to show that no more items are left to be iterated upon.

We can catch the StopIteration exception by writing the code inside the try block, catching the exception using the ‘except’ keyword, and printing it on screen using the ‘print’ keyword.

The following () method in both generators and iterators raises it when no more elements are present in the loop or any iterable object.

Examples of Python StopIteration

Given below are the examples mentioned:

Example #1

Stop the printing of numbers after 20 or printing numbers incrementing by 2 till 20 in the case of Iterators.

Code:

class printNum: def __iter__(self): self.z = 2 return self def __next__(self): if self.z <= 20: #performing the action like printing the value on console till the value reaches 20 y = self.z self.z += 2 return y else: raise StopIteration #raising the StopIteration exception once the value gets increased from 20 obj = printNum() value_passed = iter(obj) for u in value_passed: print(u)

Output:

Explanation:

In the above example, we use two methods, namely iter() and next(), to iterate through the values. The next() method utilizes if and else statements to check for the termination condition of the iteration actively.

If the iterable value is less than or equal to 20, it continues to print those values at the increment of 2. Once the value exceeds 20, the next() method raises a StopIteration exception.

Example #2

Finding the cubes of number and stop executing once the value becomes equal to the value passed using StopIteration in the case of generators.

Code:

def values(): #list of integer values with no limits x = 1 #initializing the value of integer to 1 while True: yield x x+= 1 def findingcubes(): for x in values(): yield x * x *x #finding the cubes of value ‘x’ def func(y, sequence): sequence = iter(sequence) output = [ ] #creating an output blank array try: for x in range(y): #using the range function of python to use for loop output.append(next(sequence)) #appending the output in the array except StopIteration: #catching the exception pass return output print(func(5, findingcubes())) #passing the value in the method ‘func’

Output:

Explanation:

In the above example, we find the cubes of numbers from 1 to the number passed in the function. We generate multiple values at a time using generators in Python, and to stop the execution once the value reaches the one passed in the function, we raise a StopIteration exception.

We create different methods serving their respective purposes, such as generating the values, finding the cubes, and printing the values by storing them in the output array. The program uses basic Python functions like range and append, which should be clear to the programmer in the initial stages of learning.

How to Avoid StopIteration Exception in Python?

As seen above StopIteration is not an error in Python but an exception and is used to run the next() method for the specified number of iterations. Iterator in Python uses two methods, i.e. iter() and next().

The next() method raises a StopIteration exception when the next() method is called manually.

The best way to avoid this exception in Python is to use normal looping or use it as a normal iterator instead of writing the next() method repeatedly.

Otherwise, if not able to avoid StopIteration exception in Python, we can simply raise the exception in the next() method and catch the exception like a normal exception in Python using the except keyword.

Conclusion

As discussed above in the article, it must be clear to you what is the StopIteration exception and in which condition it is raised in Python. StopIteration exception could be an issue to deal with for the new programmers as it can be raised in many situations.

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A Complete Guide To K

Introduction

In the four years of my data science career, I have built more than 80% of classification models and just 15-20% of regression models. These ratios can be more or less generalized throughout the industry. The reason behind this bias towards classification models is that most analytical problems involve making decisions. In this article, we will talk about one such widely used machine learning classification technique called k nearest neighbor (KNN) algorithm. Our focus will primarily be on how the algorithm works on new data and how the input parameter affects the output/prediction.

Note: People who prefer to learn through videos can learn the same through our free course – K-Nearest Neighbors (KNN) Algorithm in Python and R. And if you are a complete beginner to Data Science and Machine Learning, check out our Certified BlackBelt program –

Learning Objectives

Understand the working of KNN and how it operates in python and R.

Get to know how to choose the right value of k for KNN

Understand the difference between training error rate and validation error rate

What is KNN (K-Nearest Neighbor) Algorithm?

The K-Nearest Neighbor (KNN) algorithm is a popular machine learning technique used for classification and regression tasks. It relies on the idea that similar data points tend to have similar labels or values.

During the training phase, the KNN algorithm stores the entire training dataset as a reference. When making predictions, it calculates the distance between the input data point and all the training examples, using a chosen distance metric such as Euclidean distance.

Next, the algorithm identifies the K nearest neighbors to the input data point based on their distances. In the case of classification, the algorithm assigns the most common class label among the K neighbors as the predicted label for the input data point. For regression, it calculates the average or weighted average of the target values of the K neighbors to predict the value for the input data point.

The KNN algorithm is straightforward and easy to understand, making it a popular choice in various domains. However, its performance can be affected by the choice of K and the distance metric, so careful parameter tuning is necessary for optimal results.

When Do We Use the KNN Algorithm?

KNN can be used for both classification and regression predictive problems. However, it is more widely used in classification problems in the industry. To evaluate any technique, we generally look at 3 important aspects:

1. Ease of interpreting output

2. Calculation time

3. Predictive Power

Let us take a few examples to  place KNN in the scale :

KNN classifier fairs across all parameters of consideration. It is commonly used for its ease of interpretation and low calculation time.

How Does the KNN Algorithm Work?

Let’s take a simple case to understand this algorithm. Following is a spread of red circles (RC) and green squares (GS):

You intend to find out the class of the blue star (BS). BS can either be RC or GS and nothing else. The “K” in KNN algorithm is the nearest neighbor we wish to take the vote from. Let’s say K = 3. Hence, we will now make a circle with BS as the center just as big as to enclose only three data points on the plane. Refer to the following diagram for more details:

The three closest points to BS are all RC. Hence, with a good confidence level, we can say that the BS should belong to the class RC. Here, the choice became obvious as all three votes from the closest neighbor went to RC. The choice of the parameter K is very crucial in this algorithm. Next, we will understand the factors to be considered to conclude the best K.

How Do We Choose the Factor K?

First, let us try to understand exactly the K influence in the algorithm. If we see the last example, given that all the 6 training observation remain constant, with a given K value we can make boundaries of each class. These decision boundaries will segregate RC from GS. In the same way, let’s try to see the effect of value “K” on the class boundaries. The following are the different boundaries separating the two classes with different values of K.

If you watch carefully, you can see that the boundary becomes smoother with increasing value of K. With K increasing to infinity it finally becomes all blue or all red depending on the total majority.  The training error rate and the validation error rate are two parameters we need to access different K-value. Following is the curve for the training error rate with a varying value of K :

As you can see, the error rate at K=1 is always zero for the training sample. This is because the closest point to any training data point is itself.Hence the prediction is always accurate with K=1. If validation error curve would have been similar, our choice of K would have been 1. Following is the validation error curve with varying value of K:

This makes the story more clear. At K=1, we were overfitting the boundaries. Hence, error rate initially decreases and reaches a minima. After the minima point, it then increase with increasing K. To get the optimal value of K, you can segregate the training and validation from the initial dataset. Now plot the validation error curve to get the optimal value of K. This value of K should be used for all predictions.

The above content can be understood more intuitively using our free course – K-Nearest Neighbors (KNN) Algorithm in Python and R

Breaking It Down – Pseudo Code of KNN

We can implement a KNN model by following the below steps:

Load the data

Initialise the value of k

For getting the predicted class, iterate from 1 to total number of training data points

Calculate the distance between test data and each row of training dataset. Here we will use Euclidean distance as our distance metric since it’s the most popular method. The other distance function or metrics that can be used are Manhattan distance, Minkowski distance, Chebyshev, cosine, etc. If there are categorical variables, hamming distance can be used.

Sort the calculated distances in ascending order based on distance values

Get top k rows from the sorted array

Get the most frequent class of these rows

Return the predicted class

Implementation in Python From Scratch

We will be using the popular Iris dataset for building our KNN model. You can download it from here.



Comparing Our Model With Scikit-learn from sklearn.neighbors import KNeighborsClassifier neigh = KNeighborsClassifier(n_neighbors=3) neigh.fit(data.iloc[:,0:4], data['Name']) # Predicted class print(neigh.predict(test)) # 3 nearest neighbors print(neigh.kneighbors(test)[1])

We can see that both the models predicted the same class (‘Iris-virginica’) and the same nearest neighbors ( [141 139 120] ). Hence we can conclude that our model runs as expected.

Implementation of KNN in R

View the code on Gist.

Output

#Top observations present in the data SepalLength SepalWidth PetalLength PetalWidth Name 1 5.1 3.5 1.4 0.2 Iris-setosa 2 4.9 3.0 1.4 0.2 Iris-setosa 3 4.7 3.2 1.3 0.2 Iris-setosa 4 4.6 3.1 1.5 0.2 Iris-setosa 5 5.0 3.6 1.4 0.2 Iris-setosa 6 5.4 3.9 1.7 0.4 Iris-setosa #Check the dimensions of the data [1] 150 5 #Summarise the data SepalLength SepalWidth PetalLength PetalWidth Name Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100 Iris-setosa :50 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300 Iris-versicolor:50 Median :5.800 Median :3.000 Median :4.350 Median :1.300 Iris-virginica :50 Mean :5.843 Mean :3.054 Mean :3.759 Mean :1.199 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800 Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500

Step 3: Splitting the Data

View the code on Gist.

Step 4: Calculating the Euclidean Distance

View the code on Gist. View the code on Gist.

Output

For K=1 [1] "Iris-virginica"

In the same way, you can compute for other values of K.

Comparing Our KNN Predictor Function With “Class” Library

View the code on Gist.

Output

For K=1 [1] "Iris-virginica"

We can see that both models predicted the same class (‘Iris-virginica’).

Conclusion

The KNN algorithm is one of the simplest classification algorithms. Even with such simplicity, it can give highly competitive results. KNN algorithm can also be used for regression problems. The only difference from the discussed methodology will be using averages of nearest neighbors rather than voting from nearest neighbors. KNN can be coded in a single line on R. I am yet to explore how we can use the KNN algorithm on SAS.

Key Takeaways

KNN classifier operates by finding the k nearest neighbors to a given data point, and it takes the majority vote to classify the data point.

The value of k is crucial, and one needs to choose it wisely to prevent overfitting or underfitting the model.

One can use cross-validation to select the optimal value of k for the k-NN algorithm, which helps improve its performance and prevent overfitting or underfitting. Cross-validation is also used to identify the outliers before applying the KNN algorithm.

The above article provides implementations of KNN in Python and R, and it compares the result with scikit-learn and the “Class” library in R.

Frequently Asked Questions Related

A Complete Guide To Pytorch Tensors

Introduction to PyTorch Tensors

The following article provides an outline for PyTorch Tensors. PyTorch was released as an open-source framework in 2023 by Facebook, and it has been very popular among developers and the research community. PyTorch has made building deep neural network models by providing easy programming and faster computation. However, PyTorch’s strong feature is providing Tensors. Tensors are defined as single dimensions or a matrix of a multi-dimensional array containing an element of single data types.

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Tensors are also used in the Tensorflow framework, which Google released. NumPy Arrays in Python are basically just tensors processed by using GPUs or TPUs for training neural network models. PyTorch has libraries included in it for calculating gradient descent for feed-forward networks as well as back-propagation. PyTorch has more support to the Python libraries like NumPy and Scipy compared to other frameworks like Tensorflow.

PyTorch Tensors Dimensions

In any linear algebraic operations, the user may have data in vector, matrix or N-dimensional form. Vector is basically a single-dimensional tensor, Matrix is two-dimensional tensors, and an Image is a 3-dimensional tensor with RGB as a dimension. PyTorch tensor is a multi-dimensional array, same as NumPy and also it acts as a container or storage for the number. To create any neural network for a deep learning model, all linear algebraic operations are performed on Tensors to transform one tensor to new tensors.

Example:

import torch tensor_1 = torch.rand(3,3)

Here random tensor of size 3*3 is created.

How to Create PyTorch Tensors Using Various Methods

Let’s create a different PyTorch tensor before creating any tensor import torch class using the below command:

Code:

import torch

1. Create tensor from pre-existing data in list or sequence form using torch class.

It is a 2*3 matrix with values as 0 and 1.

Syntax:

torch.tensor(data, dtype=None, device=None, requires_grad=False, pin_memory=False)

Code:

import torch tensor_b = torch.Tensor([[0,0,0], [1,1,1]]) tensor_b

Output:

2. Create n*m tensor from random function in the torch.

Syntax:

torch.randn(data_size, dtype=input.dtype, layout=input.layout, device=input.device)

Code:

import torch tensor_a = torch.rand((3, 3)) tensor_a

Output:

3. Creating a tensor from numerical types using functions such as ones and zeros.

torch.zeros(data_size, dtype=input.dtype, layout=input.layout, device=input.device)

Code:

tensor_d = torch.zeros(3, 3) tensor_d

Output:

In the above, tensor .zeros() is used to create a 3*3 matrix with all the values as ‘0’ (zero).

4. Creating a PyTorch tensor from the numpy tensor.

To create a tensor from numpy, create an array using numpy and then convert it to tensor using the .as_tensor keyword.

Syntax:

torch.as_tensor(data, dtype=None, device=None)

Code:

import numpy arr = numpy.array([0, 1, 2, 4]) tensor_e = torch.as_tensor(arr) tensor_e

Output:

Here is the basic tensor operation to perform the matrix product and get a new tensor.

Code:

tensor_e = torch.Tensor([[1, 2], [7, 8]]) tensor_f = torch.Tensor([[10], [20]]) tensor_mat = tensor_e.mm(tensor_f) tensor_mat

Output:

Parameters:

Here is the list and information on parameters used in syntax:

data: Data for tensors.

dtype: Datatype of the returned tensor.

device: Device used is CPU or CUDA device with returned tensor.

requires_grad: It is a boolean data type with values as True or False to record automatic gradient on returned tensor.

data_size: Data shape of the input tensor.

pin_memory: If the pin_memory is set to Truly returned tensor will have pinned memory.

See below jupyter notebook for the above operation to create tensors.

Importance of Tensors in PyTorch

Tensor is the building block of the PyTorch libraries with a matrix-like structure. Tensors are important in PyTorch framework as it supports to perform a mathematical operation on the data.

Following are some of the key important points of tensors in PyTorch:

Tensors are important in the PyTorch as it is a fundamental data structure and all the neural network models are built using tensors as it has the ability to perform linear algebra operations

Tensors are similar to numpy arrays, but they are way more powerful than the numpy array as They perform their computation GPU or CPU. Hence, It is way more faster than the numpy library of python.

It offers seamless interoperability with Python libraries so that the programmer can easily use Sci-kit, SciPy libraries with tensors. Also, using functions like as_tensors or from_numpy programmer can easily convert the numpy array to PyTorch tensors.

One of the important features offered by tensor is it can store track of all the operations performed on them, which helps to compute the gradient descent of output; this can be done using Autograd functionality of tensors.

It is a multi-dimensional array which holds data for Images that can be converted into a 3-dimensional array based on its color like RGB (Red, Green and Blue); also, it holds Audio data or Time series data; any unstructured data can be addressed using tensors.

Conclusion

To learn PyTorch framework for building deep learning models for computer vision, Natural language processing or reinforcement learning. In the above tutorial, a programmer can get an idea of how useful and simple it is to learn and implement tensors in PyTorch. Of course, tensors can be used in PyTorch as well as Tensorflow. Still, the basic idea behind using tensors stays the same: using GPU or CPU with Cuda cores to process data faster which one framework to use for building models is developers decisions. Still, the above articles give a clear idea about tensor in the PyTorch.

Recommended Articles

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

The Complete Guide To Todoist Filters

If you’re already using Todoist to keep track of your life, you might wonder how you can make it even more useful. The simple answer: Todoist filters. These have the power to streamline and better organize all your tasks, especially when you’ve added so many to-dos that you don’t even know where to start. The good news is you can use built-in filters or create your own. Read on to learn more.

What Are Todoist Filters?

Todoist already has a handy search bar to quickly find tasks. Todoist filters, though, take it a step further by letting you create custom searches for those you use often. For example, you might create a filter for calls or emails you need to respond to by the end of the day. You can filter by tag and due date to quickly see just those tasks.

If you have a few hundred tasks on your to-do list, simply scrolling through isn’t enough. Even if you carefully categorize them with tags and priority, you could still waste valuable time trying to find what you need and could easily miss an important task.

To really see how useful Todoist filters can be, let’s imagine a busy professional has several hundred tasks listed for the week. This could be a mixture of emails, calls, projects, and even things to do on their way home. When they log in to see their tasks at the start of the day, they want to get to work immediately.

They create a filter to first show only top priority tasks. They further customize the filter to show tasks that are due that day, possibly even tasks due before lunch. If they always handle emails left over from the day before the first thing in the morning, they’d customize the filter one more time to only show email tasks. Suddenly, that extremely long list only shows the handful of tasks the person needs to do as soon as they start working that day.

The same holds true for when they leave for the day. They’d filter tasks by Home along with the current day. They could also filter by person if they wanted to see upcoming tasks (such as extracurricular school activities) for their kids, spouse, friends, or charity organizations.

Best Default Todoist Filters

By default, Todoist gives you a few filters. These may vary based on the platform you’re using. For the purpose of this post, I’m using the free Web version.

The following filters are included by default without the need for you to create anything:

Assigned to me – only lists tasks that are assigned to you

Priority 1 – lists tasks labeled as Priority 1

No due date – only lists tasks without a due date

View all – shows all your tasks in one list

Out of these defaults, Priority 1 and Assigned to me are probably the most useful, as you can quickly see what your more urgent tasks may be.

Creating Your Own Filters

In the grand scheme of things, the default Todoist filters are extremely basic and may not be all that helpful. That’s when it’s best to create your own filters.

To make filters better, it’s important to use labels, dates (if applicable), and priorities when creating tasks. Otherwise, it’s difficult to create filters based on those criteria. You can create labels when creating or editing a task or by using the “Filters & Labels” section.

To create your own filter, select “Filters & Labels” in the left pane. On Android, drag the menu up from the bottom and select “Filters.” In iOS, tap “<” to open the menu and select “Filters & Labels.”

Beside “Filters,” select the “+” button to add a new filter. (For this example, I’m creating a filter that shows overdue tasks. This works well for those tasks that get overlooked but still need to be done. This only works if your tasks have a due date.)

When creating basic filters, there are a few things to keep in mind:

If your query is based on a label, always use “@” symbol before the label name, such as “@work.”

If your query is based on a project/main section or only a sub-section, always use “#” before the name, such as “#Inbox.”

If you want your query to include a main section along with all of its sub-sections, use “##” before the name.

If you want to exclude a specific sub-section, add a “!” before the sub-section name, such as “##Inbox & !#Followups.” (This includes all sections in the Inbox parent section, excluding anything from the Followups sub-section).

If you want to search sections with the same name across multiple projects, use “/” before the name, such as “/Emails,” which could be a sub-section in multiple parent sections.

Creating Advanced Todoist Filters

Creating a basic filter is fairly easy. Simply use the name of a label, section, date, or specific word or phrase (such as overdue, recurring, no date, no label). However, you’re not limited to a single filter criteria. For example, in the section above, you saw how to exclude a sub-section in a filter.

To use multiple criteria, use the following operators:

“*” (wildcard) – Make your filter more encompassing with a wildcard symbol. For instance, search for all tasks assigned to anyone with the last name Crowder with “assigned to: * crowder.”

If you love creating search filters in all the productivity apps you use, learn how to master VLOOKUP in Excel and Google Sheets.

Most Useful Filters

To help you get started, Todoist has an AI filter query generator. It may not get things quite right but can give you a starting point.

If you’re not sure where to start to create your own filters, consider using some of the most useful filter queries, including:

Finding Todoist Filter Inspiration

Want to become a master of Todoist filters? All you need is the right inspiration. The Doist blog has 24 incredible and highly useful filters to get you organized quickly. These are also great examples of using more complex filters.

Frequently Asked Questions 1. How can I access my most used filters faster?

If you only have a few filters, going to the “Filters & Labels” section isn’t a problem. However, if you have dozens of filters, finding the right one can be time consuming.

2. Can I filter completed tasks? 3. Do I have to create a filter for all my searches? 4. How can I organize my filters?

It’s easy for filters to get out of hand. There are several ways to keep them organized:

Add your most used to Favorites.

Group similar filters with color-coded labels.

Drag and drop to organize filters the way you want in your Filters & Labels list.

If there are filters you no longer use, delete them. The fewer filters you have, the easier it is to find what you need.

Crystal Crowder

Crystal Crowder has spent over 15 years working in the tech industry, first as an IT technician and then as a writer. She works to help teach others how to get the most from their devices, systems, and apps. She stays on top of the latest trends and is always finding solutions to common tech problems.

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Complete Guide On Independent Director

Introduction to Independent Director

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Who is the Independent Director?

An independent director is a member of the board of directors of a company who does not participate in the daily chores of the entity’s operations and does not have a material relationship with the company. Thus, he is also referring as the non-executive director of the company.

The word “independent” attains its importance only when the board of directors confirms that the directors do not have any material relationship with the company. Material relationship refers to a business relationship between a person and a limited benefit company transacting through self, a family member, or an officer wherein the person has some beneficial interest.

Material relations can be in the form of direct control of the other entity, being a partner or shareholder or an officer of another entity. Related-party transactions are generally allowed but only up to a certain level.

These provisions apply to the listed entities. In addition, the parent or subsidiary of such a listed company also covers these provisions.

Role of Independent Director

The independent director plays a passive role in day-to-day activities but plays an active role in the committees set up by the company. In addition, he is responsible for managing the risks and ensuring corporate governance standards within the company.

He holds a vital role in succession planning for the company.

He ensures a balance between the conflicting interest of the stakeholders.

Independent Director is Mentor and guides the company since he possesses expertise in one of the primary business areas.

He is responsible for being objective while evaluating the performance of the directors.

Independent Directors are vital in ensuring the integrity of the financial controls and systems.

He is involved in the key strategy-making process, appointing key persons, designing the standards of conduct, and risk management.

Should provide levels within which remuneration can pay to the key managerial personnel of the entity and other executive directors.

He provides solutions in case of conflicts between the management and the interest of the company’s shareholders.

Responsibility of Independent Director

He Should try to attend the general meeting of the company and meetings of the board of directors.

He should act within the authority provided to him.

Attend the committee meetings wherein he is the committee’s chairperson.

Should have enough knowledge of the company and the environment surrounding the company.

Independent directors should take care of the interests of the company, its shareholders, and its employees.

He is responsible for reporting the actual or suspected chances of fraud or any willful violation of the company’s code of ethics.

He holds critical information such as technologies, promotion plans, price-sensitive knowledge, and business secrets. And should keep the information confidential unless expressly allowed to disseminate by the board or required under the law.

He should safeguard the interest of the stakeholders, especially the minority ones.

He should ensure that the vigil mechanism is adequate and functional at all times and the person using the facility is free from any risk due to using such facilities.

Applicability on appointing Independent Director

At the time of appointment of an independent director, the board needs to determine whether the person has any material relationship with the company.

In doing so, the board checks that the person’s relationship falls within the familial, accounting, consulting, commercial, banking, charitable, or legal category.

If the board successfully determines that no such relationship exists, the person is eligible for appointment as an independent director of the company.

Provisions related to Independent Director

Is an employee, or is an immediate family member of the company’s executive director?

A partner of an employee of internal or external company auditors, whether present or former auditor.

Is an immediate family member of the partner or employee of such auditors in clause b.

Is in receipt of compensation of more than US $ 120,000 per annum for services other than being a director of the listed company. However, such compensation should not be contingent on the continued services and not concern the pension or deferred compensation for prior services.

Is an immediate family of the person as specified in clause d.

Is an employee of the company who receives or makes payments to the listed company concerning the property or services for an amount that exceeds 2% of the company’s consolidated gross revenues or the US $ 1 million in any of the preceding three fiscal years.

An immediate family member of the company’s executive officer as specified in clause f.

Also, if the person has had such a relationship as above in the last three years, the person would not be qualified as an independent director. Thus, the criteria limit is three years preceding the year of appointment.

Companies listed on the New York Stock Exchange and Nasdaq must comply with stricter norms about the independence criteria. Such companies should ensure that the director’s ability to remain independent is not affected by any material relationships, including the impact of any compensatory fee paid or being affiliated with the subsidiary or affiliate of a subsidiary of the company.

If the company imposes additional independence standards, it must disclose the same.

As per NYSE listing rules, the listed companies in the US should have the majority of directors as independent directors.

Benefits

The business transactions are fair without the dent of being biased toward directors.

Stakeholders’ interest is protected.

Independent directors are free from undue influence from the management.

The expertise of the independent directors can be used for the company’s benefit.

The performance evaluation of the directors is objective and without any bias.

They are essential to good corporate governance policies.

He helps in the succession planning of the company.

He resolves the conflicts between the shareholders and the management.

The company has information asymmetry since independent directors do not know about the daily chores. Thus, the information received by such a director is some systematic noise.

An Independent director is still a director, and his decisions are subject to approval from the board members.

Due to asymmetry in the information exchange, ad hoc invitations to the board meetings do not suffice for the purpose. Thus, a completely independent board would work with poor information, and the objective of independence would be ineffective.

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The Complete Guide To Successful Branding In 2023

Successful branding never just happens. Give the best experience to your consumers when they make (or consider) a purchase. Learn the following tips to get there.

The Complete Guide to Successful Branding in 2023

Products represent a lot more than simply something that customers buy to satisfy needs and desires. Oftentimes, they are also a means for businesses to promote particular expertise to shoppers. That is precisely why firms go out of the methods to cultivate a particular new image.

According to one study, less than 35 percent of consumers actually trust the brands that they buy from.

1. Branding in a Nutshell

Before you build a brand, you want to choose what a brand actually is. As you may believe that it’s hardly more than the title and emblem of a business, in addition, it comprises the company’s voice and attitude. Consumers interact with brands if they buy into them or not, so they are building an experience every time they have some contact with you.

Branding is the procedure of employing a particular attribute to a company in the hopes that customers will associate a specific attribute with said firm. A high degree of brand awareness results in an organization’s image being viewed as popular. Consumers can not look at buying goods and services out of a particular business if they don’t know it is present.

Because of this, you’re going to want to concentrate on building brand awareness before you do anything else. As you may believe that a massive publicity stunt would be the very best means to accomplish this, that may not be the situation.

According to specialists from DesignBro, working with a company that may help you build a new identity from the ground up could possibly be important. Consider the number of companies you are able to recall-based only on their comparatively straightforward glossy logos. Probably, you are able to visualize quite a couple.

Remember that visual design is simply some of everything you want. You will want to set up a target market so that you understand what your group is assumed to be aiming for.

2. Honing in On a Single Target

Since branding can potentially lead to trust, you’ll want to better understand who your brand will speak to. Take some time to figure out what kind of consumer needs your product and figure out what other problems they might need solutions to.

Some specialists construct a research-based outline of a prototypical client they predict a buyer character. As you do not have to go this way, it will help to have a better grasp of what your clients need before you proceed any further.

Set a mission statement that spells out the reason you established your small business. Yet more, you do not have to go to this extreme. You do, but you wish to construct a brand that you genuinely believe in and use your personal beliefs to form the messages which you feed your prospective customers.

Hard amounts, if you’re able to find them, would be the ideal method to have a better picture of that may eventually purchase into your own brand. Startup companies frequently make the mistake of dismissing market evaluation reports and dip themselves deep into debt in the procedure.

Pay careful attention to whatever information you may find about your possible customer. If you can not say explicitly what type of person may want to purchase your goods, then it’s easy to envision that nobody could.

That may sound harsh, but it is true. Your brand is designed to give aid to individuals no matter if they understand they need assistance in some manner. Consider all the many surfer-themed fashion brands which you have seen come into vogue during the past 10-15 decades.

Just how a lot of those ever brought a marketplace of hardcore surfers? The solution is probably not one of these, but it does not matter since they could supply a laid-back image to folks who desired a means to escape from their everyday lives. You will want to locate a hidden want such as that and tap it to make sure that your brand reaches the best number of people possible.

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3. Defining Your Company’s Values

As customer confidence in important companies has been eroding, you have to work out a method to make people wish to think in the picture which you are promoting. That is hard if you do not think about your brand. Be certain that you clearly outline what you believe in and the way you believe that your brand stands apart from everybody else in the business section you cope with.

They are in a position to place themselves as a much healthier alternative to mass-market businesses. The ones that sell products which promise to be better for the environment also has done well to place themselves apart from the rest.

Considering the increased emphasis on solitude, you may wish to think about boosting your institution’s dedication to protecting your customers. This is particularly true when you are running a committed social networking marketing effort.

4. Deploying Your Finished Brand Image

Maybe the first thing to do if you finalize your new image is accomplished that no manufacturer is ever really finished. The general public perception of your organization will probably continue to evolve in the long term. This means you are going to want to utilize your branding substances on everything your business puts out. Ensure that your packaging and goods are all branded suitably.

On the flip side, you are going to want to go all out by means of your brand image. Each and every profile photograph and piece of this cover artwork on your website and societal networking accounts must reflect your own brand. Perhaps you will wish to set your logo as your own profile picture because this will make it much easier for your clients to recognize your own firm.

All your articles and captions must represent the exceptional voice you’ve came up with for your own brand. If your manufacturer is snarky, then you will want your articles to reflect that. As you do not wish to be argumentative for debate’s sake, there is no reason why you can not distinguish your brand by showcasing your distinctive sense of humor.

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5. Framing Advertisements Through Your Brand’s Image

Those beginning a committed email marketing campaign are going to want to concentrate on producing a first impression, for example, so they can be certain any follow-ups will probably be well received.

Ask yourself the way your manufacturer would present itself and then use the response to that question for a framework of reference when replying to your potential customers. Some business experts have used this approach to think of private brands, which ought to help illustrate exactly how successful these techniques are.

Think about what someone might say about your company after they met your brand for the first time. You may want them to describe it using some specific words. As soon as you know what kind of adjectives you’d like to hear consumers using to talk about your brand, you’ll be in a much better position to figure out the best way to reach them.

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