Trending February 2024 # Examples And Functions Of Python Numpy.diff() # Suggested March 2024 # Top 4 Popular

You are reading the article Examples And Functions Of Python Numpy.diff() updated in February 2024 on the website Flu.edu.vn. We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested March 2024 Examples And Functions Of Python Numpy.diff()

Introduction to numpy.diff()

numpy.diff() is a function of the numpy module which is used for depicting the divergence between the values along with the x-axis. So the divergence among each of the values in the x array will be calculated and placed as a new array. These difference values for the arrays can be calculated across up to n number of times. so this means the disparity between the given values can be effectively estimated across multiple levels of the arrays.

Start Your Free Software Development Course

Web development, programming languages, Software testing & others

a = The array which is keyed in for determining the difference across the elements of the array.

n = Represents the entire number of times the differentiation process needs to be carried upon.

append, prepend = If some value needs to be appended or prepended to the values in the x-axis then these parameters are been used. here the difference will be calculated just before the append or the prepend process.

How numpy.diff() Works?

Consider an input array Test, the occurrence of the first difference for the input array is calculated using the formula out[i]=Test[i+1]-a[i]. The diff() again on this array helps to calculate the higher difference values.

The numpy is used on top of a one-dimensional numpy array. here the one-dimensional array has only a single-dimensional set of elements to it. But there could be instances where it needs to be substituted over a two-dimensional array too. from a different perception, there are situations where the numpy element need to be substituted over a two-dimensional array also. these two-dimensional arrays are in other words termed as multiple axes.

So at the point of implying diff() function over two-dimensional axis arrays then we rely on the use of the argument called an axis. here the value specified for the axis argument will be representing the columns of the two-dimensional array. so as like mentioned before at the point of applying the diff() function execution the initial column in the array undergoes a transformation which is very similar to below.

Sample array,

[0, 1, 1], [7, 3, 15], [8, 3, 11]

[0, 1, 1], [7, 3, 15], [8, 3, 11]

Difference value for the first row in the two-dimensional array will be taken forward as like below.

[0 1 1] difference = [1 0]

[1 0] difference = [-1]

The same process will be extended upon each and every row in the array,

Examples of numpy.diff()

Following are the examples are given below:

Example #1 import numpy as np Date_array = np.arange('2024-09-01', '2024-09-05', dtype=np.datetime64) output_diffrence = np.diff(Date_array) print("The difference in date value is: " + str(len(output_diffrence)) +" days ")

Output:

Code Explanation: The above program uses numpy library for determining the difference of days within two date values in an array. the program begins with an import of the numpy library using the alias name as np. the np.arrange() method is used for creating an array element, the array element formulated in the arrange function is based on the below syntax.

numpy.arange([start_value, stop_value, n_value,dtype=None)

here the first two indexes refer to the start and stop value whereas the third type mentions the data type which is been used. In this case for determining the expanded array of dates, the arrange method is filled with the start and the end date values. Both the date values are maintained in the YYYY-MM-DD format. so all dates falling within this are determined and formulated as an array. the formulated array is passed as input to the np.diff() function. so the np.diff() function is responsible for determining the difference in date values between each and every item in the formulated array. so once the difference value is determined it is depicted as an array. we have additionally used the len() function to determine the length of the array. based on the length of the array the number of days is determined. The determined number of days is populated in the print console as output.

Example #2

Code:

import numpy as np array_var = np.array([1,2,5]) print("Array value:",array_var) output_diffrence = np.diff(array_var) print("The diffrence value is: ",output_diffrence )

Output :

Next, the array variable is passed as input to the np.diff() function. As we already know this np.diff() function is primarily responsible for evaluating the difference between the values of the array. the degree of difference can be depicted next to this parameter. based on the degree of difference mentioned the formulated array list will get hierarchal determined for its difference. the derived output is printed to the console by means of the print statement.

Example #3

Code:

import numpy as np array_var = np.array([[1, 7, 4, 12], [4, 2, 4, 8]]) print("Array value:",array_var) output_diffrence = np.diff(array_var,n=2) print("The diffrence value is: ",output_diffrence )

Output :

Code Explanation: The given program is used for formative the disparity of value between a given set of two different arrays, So the program starts with a header import of numpy module as alias name np. the array function is used for creating an array with the necessary set of values and data types are handily specified. Again as like before here too the returned value of the array function is stored in a variable of name ‘array’. This particular variable is accountable for holding the array values and the value held by this array set of the variable is displayed in the console. The necessity for performing the array function conversion in these diff() function processing is because there is a need to convert a list of values passed into a-axis oriented representation.

Conclusion

The above content nicely mentions the necessity of np.diff() function in python oriented programming for axis level difference calculation. Also additionally a set of three different techniques for implementing np.diff() is also discussed.

Recommended Articles

This is a guide to numpy.diff(). Here we discuss the Introduction and working of python numpy.diff() along with different examples and its code implementation. You may also look at the following articles to learn more –

You're reading Examples And Functions Of Python Numpy.diff()

Understanding Floor And Ceiling Functions In Python

The floor and ceiling functions are mathematically used to round numbers to the nearest integer. This comprehensive guide will explore the floor and ceiling functions in Python, understand their formulas, and discover their various use cases and applications. Additionally, we will delve into the behavior of these functions and highlight common mistakes to avoid when working with them.

Dive into the world of floor and ceiling functions in Python. Learn their formulas, implementation methods, use cases, behavior, and common mistakes to avoid. Enhance your understanding of math floor in Python and math ceiling functions.

What is the Floor Function? 

The floor function, denoted as floor(x) or ⌊x⌋, returns the largest integer less than or equal to x. It rounds down a given number to the nearest whole number. Let’s explore the formula and implementation of the floor function in Python.

Formula: floor(x) = ⌊x⌋

Python Implementation import math x = 3.8 floor_value = math.floor(x) print("Floor value of", x, "is", floor_value) Output

A floor value of 3.8 is 3

Floor Function Formula Derivation

The floor function satisfies the identity

(1)

for all integers n

A number of geometric-like sequences with a floor function in the numerator can be done analytically. For instance, sums of the form

(2)

can be done analytically for rational x. For x=1/m  a unit fraction

(3)

Sums of this form lead to Devil’s staircase-like behavior.

For irrational , and 

(4) (5)

What is the Ceil Function? 

The smallest integer bigger than or equal to ⌈x⌉ is the result of the ceil function, denoted by ceil(x) or x. A given number is rounded to the next whole number. We will discuss the formula and implementation of the ceil function in Python.

Formula: ceil(x) = ⌈x⌉

Python Implementation import math x = 3.2 ceil_value = math.ceil(x) print("Ceil value of", x, "is", ceil_value) Output

Ceil value of 3.2 is 4

Floor vs Ceil Function

The ceiling and floor functions will be compared in this section, along with their main similarities and situations to apply both.

While the ceiling function rounds up to the nearest integer, the floor function rounds down to the closest. For instance, the it yields 3 for the integer 3.5, but the ceiling function returns 4.

The floor function is handy when values need to be rounded down, like when determining the number of whole units. On the other hand, the ceil function is handy when rounding up is required, like when allocating resources or determining the minimum number of elements.

Also Read: Functions 101 – Introduction to Functions in Python For Absolute Begineers

Use Cases and Applications Floor Function

Explore real-world applications of this function across various domains, including finance, data analysis, and computer science.

In finance, the floor function is used for mortgage calculations to determine the minimum monthly payment required based on interest rates and loan duration.

The floor function can be employed in data analysis to discretize continuous variables into discrete intervals for easier analysis and visualization.

In computer science, the floor function is useful in algorithms involving dividing or partitioning resources among multiple entities.

Use Cases and Applications of the Ceil Function 

Discover the practical applications of the ceil function in different fields, such as mathematics, statistics, and programming.

The ceil function is used in mathematics to compute the least integer greater than or equal to a given number, essential in various mathematical proofs and calculations.

In statistics, the Ceil function is employed in rounding up sample sizes or determining the required observations for statistical tests.

In programming, the ceil function finds application in scenarios such as rounding up division results, handling screen pixel dimensions, or aligning elements within a grid system.

Must Read: Data Analysis Project for Beginners Using Python

Understanding the Behavior of the Floor Function 

Gain insights into the behavior of the floor function, including its handling of positive and negative numbers, fractions, and special cases.

It is always rounds down, even for negative numbers. For example, floor(-3.8) returns -4.

It rounds down fractions to the nearest integer. For instance, floor(3.8) and floor(3.2) return three.

It yields the same result when the input is already an integer. As an illustration, floor(5) returns 5.

Understanding the Behavior of the Ceil Function 

Deepen your understanding of the ceil function’s behavior, including its treatment of positive and negative numbers, fractions, and specific scenarios.

The ceil function always rounds up, even for negative numbers. For example, ceil(-3.8) returns -3.

The ceil function rounds up fractions to the nearest integer. For instance, ceil(3.8) and ceil(3.2) result in 4.

The ceil function yields the same result whether the input is an integer. Ceil(5), for instance, returns 5.

Common Mistakes to Avoid While Using the Floor Function

Identify and rectify common mistakes made when utilizing the floor function in Python. Learn best practices and troubleshooting techniques.

Import the math floor in Python module before using the floor function.

Using the floor function incorrectly in situations that require rounding to a specific decimal place.

Confusing the floor function with other rounding functions, such as round or trunc.

Common Mistakes to Avoid While Using the Ceil Function 

Discover common pitfalls encountered when working with the ceil function in Python and acquire strategies to overcome them.

Neglecting to import the math module before using the ceil function.

Using the ceil function incorrectly when rounding down is required.

Confusing the ceil function with other rounding functions or integer division.

Conclusion

In conclusion, understanding Python’s floor and ceiling functions is essential for precise number rounding and various mathematical operations. By mastering these functions, you will enhance your mathematical and programming skills. Remember to utilize these functions accurately, considering their behavior and use cases. Keep exploring and applying these functions in your Python projects to unlock their full potential.

Leverage your Python skills to start your Data Science journey with our Python course for beginners with no coding or Data Science background.

Excellence in Python is one of the most essential things for making a successful career in Data Science. Enroll in our BlackBelt Program to master python, learn best data techniques and boost your career!

Frequently Asked Questions

Q1. What is the difference between floor and ceiling functions in Excel?

A. In Excel, the floor and ceiling functions round numbers down or up, respectively, to the nearest integer.

Q2. What is the floor 2.4 ceil 2.9 equal to?

A. The result of floor 2.4 ceil 2.9 is 2 and 3, respectively.

Q3. What is the function of a floor function?

A. The floor function returns the largest integer less than or equal to a given number.

Related

Guide To Namedtuple Python With Examples

Introduction to Namedtuple Python

Web development, programming languages, Software testing & others

Working of Namedtuple

As tuple has an integer index to access as they have no names, there might be ambiguity to store and access the data to that particular integer index. Tuples are an ad-hoc structure that means if two tuples have the same number of fields and the same data stored, there is an ambiguity for accessing. So to overcome all such problems, Python introduces Namedtuple in the collections module. Python has containers like dictionaries called namedtuple(), supporting access to the value through key similar to dictionaries. Namedtuple is the class of collections module which also contains many other classes for storing data like sets, dictionaries, tuple, etc.

Nametuple is an extension to the built-in tuple data type, where tuples are immutable, which means once created, they cannot be modified. Here it shows how we can access the elements using keys and indexes. To use this namedtuple(), you should always import collections in the program. Namedtuples offers a few users access and conversion methods that all start with a _ underscores. Namedtuple is mostly used on unstructured tuples and dictionaries, which makes it easier for data accessing. Namedtuple makes an easy way to clean up the code and make it more readable, which makes it a better structure for the data.

There are different access and conversion operations on namedtuple.

They are as follows:

Access operations on Namedtuple() which we can access values using indexes, keys, and getattr() methods.

Access by index: In this, the values are accessed using index number because the attribute values of namedtuple() are in order so indexes can easily access it.

Access by keys: In this, the working is similar to a dictionary where the values can be accessed using the keys given as allowed in dictionaries.

Access using getattr(): This is one of another method in which it takes namedtuple and key-value as its argument.

Examples of Namedtuple Python

Given below are the examples mentioned:

Example #1

Code:

import collections Employee = collections.namedtuple('Employee',['name','age','designation']) E = Employee('Alison','30','Software engineer') print ("Demonstration using index, The Employee name is: ",E.name) print ("Demonstration using keynames, The Employee age is : ",E[1]) print ("Demonstration using getattr(), The Employee designation is : ",getattr(E,'designation'))

In the above example, firstly, we create namedtuple() with tuple name as “Employee”, and it has different attributes in the tuple named “name”, “age”, “designation”. The Employee tuple’s key-value can be accessed using 3 different ways, as demonstrated in the program.

There are some conversion operations that can be applied on namedtuple().

They are as follows:

_make(): This function converts any iterable passed as argument to this function to namedtuple() and this function returns namedtuple().

_asdict(): This function converts the values of namedtuple that are constructed by mapping the values of namedtuple and returns the OrderDict().

** (double star) operator: This operator is used to convert to namedtuple() from the dictionary.

_fields: This function returns all the keynames of the namedtuple that is declared. We can also check how many fields and which fields are there in the namedtuple().

_replace(): This function replaces the values that are mapped with keynames that are passed as an argument to this function.

Example #2

Code:

import collections Employee = collections.namedtuple('Employee',['name','age','designation']) E = Employee('Alison','30','Software engineer') El = ['Tom', '39', 'Sales manager' ] Ed = { 'name' : "Bob", 'age' : 30 , 'designation' : 'Manager' } print ("The demonstration for using namedtuple as iterable is : ") print (Employee._make(El)) print("n") print ("The demonstration of OrderedDict instance using namedtuple is : ") print (E._asdict()) print("n") print ("The demonstration of converstion of namedtuple instance to dict is :") print (Employee(**Ed)) print("n") print ("All the fields of Employee are :") print (E._fields) print("n") print ("The demonstration of replace() that modifies namedtuple is : ") print(E._replace(name = 'Bob'))

Output:

The above program creates the namedtuple() “Employee” and then it creates iterable list and dictionary “El” and “Ed” which uses the conversion operations _make() which will convert the “El” to namedtuple(), _asdict() is also used to display the namedtuple() in the order that is using OrderDict(), the double start (**) which converts dictionary “Ed” to namedtuple(), E.fields which will print the fields in the declared namedtuple(), E.replace(name = “Bob”) this function will replace the name field value of the namedtuple() in this it replaces “Alison” to “Bob”.

Conclusion

In Python, we use namedtuple instead of the tuple as in other programming languages as this is a class of collections module that will provide us with some helper methods like getattr(), _make(), _asdict(), _fileds, _replace(), accessing with keynames, ** double star, etc. This function helps us access the values by having keys as the arguments with the above different access and conversion functions on the namedtuple() class of collections module. It is easier than tuples to use and is very efficient and readable than tuples.

Recommended Articles

This is a guide to Namedtuple Python. Here we discuss the introduction, working of namedtuple python along with examples. You may also have a look at the following articles to learn more –

Presiding Officer Of The Parliament: Role, Powers And Functions

Introduction

In Indian Parliament, there is huge importance of Presiding Offer for regulating, controlling and ensuring the proceedings of the Parliament should have conducted in orderly manner. Even for Competitive exams, the role of presiding officer is more important.

If you are looking for gaining the all the knowledge about presiding officer of the parliament, then please stay with us till the end of this article, because we are going to provide you all the related information about the same.

So, let’s start-

Who is Presiding Officer of the Parliament?

The presiding officer of the Parliament of India depends on the chamber in question.

In the Lok Sabha (House of the People), the presiding officer is the Speaker. The Speaker is elected by the members of the Lok Sabha from among themselves.

In the Rajya Sabha (Council of States), the presiding officer is the Chairman or Chairperson. The Vice President of India is the ex-officio Chairman or Chairperson of the Rajya Sabha. In the absence of the Vice President, the Deputy Chairman or the panel of Vice Chairmen presides over the proceedings of the Rajya Sabha.

The presiding officer’s role is to ensure that the proceedings of the Parliament are conducted in an orderly manner and that the rules and procedures of the respective Houses are followed.

Roles of the Presiding Officer of the Parliament

Following are the roles of the Presiding officer of the parliament:

Chairing and directing the proceedings of the House.

Maintaining order and discipline in the House.

Ensuring that the rules of the House are followed.

Protecting the rights and privileges of Members of Parliament.

Adjudicating on points of order and parliamentary procedure.

Representing the House in its external relations.

Signing bills and other official documents.

Administering oaths and affirmations to Members of Parliament.

Calling Members to order if they breach parliamentary privilege.

Overseeing the management and maintenance of parliamentary buildings and facilities.

Powers of the Presiding Officer of the Parliament

Here we have added a few of most important powers of the Presiding Officer of the Parliament, often referred to as the Speaker:

Presiding over debates and discussions in the House of Parliament.

Maintaining order and decorum in the House of Parliament.

Deciding who has the right to speak during debates.

Ruling on points of order and parliamentary procedure.

Enforcing the rules and procedures of the House of Parliament.

Suspending or expelling Members of Parliament (MPs) who breach parliamentary privilege.

Interpreting and applying the Standing Orders (rules of the House).

Admitting strangers (non-MPs) to the House of Parliament.

Summoning MPs to the House of Parliament.

Resolving disputes between MPs.

Certifying bills that have been passed by the House of Parliament.

Declaring vacancies in the House of Parliament.

Representing the House in its external relations.

Appointing and overseeing the work of House officials, such as the Clerk and the Sergeant-at-Arms.

Making administrative decisions relating to the management of parliamentary facilities and services.

Functions of the Presiding Officer of the Parliament

These are the functions of Presiding officers of the parliament that are expected to be done by the presiding officer of the parliament:

Presiding over the debates and discussions in the House of Parliament.

Maintaining order and discipline in the House of Parliament.

Ensuring that parliamentary procedures and rules are followed.

Making rulings on points of order and parliamentary procedure.

Enforcing parliamentary privilege and protecting the rights and privileges of MPs.

Determining which MPs have the right to speak during debates and discussions.

Managing the parliamentary agenda and ensuring that parliamentary business is conducted efficiently.

Representing the House in its external relations, such as with other branches of government, international organizations, and foreign dignitaries.

Signing bills and other official documents that have been passed by the House of Parliament.

Administering oaths and affirmations to new MPs when they are elected to the House.

Calling MPs to order if they breach parliamentary privilege.

Resolving disputes between MPs.

Certifying bills that have been passed by the House of Parliament.

Admitting strangers (non-MPs) to the House of Parliament.

Summonsing MPs to the House of Parliament.

Overseeing the work of House officials, such as the Clerk and the Sergeant-at-Arms.

Making administrative decisions relating to the management of parliamentary facilities and services.

Advising the government and opposition on parliamentary procedures and practices.

Representing the House at public events and ceremonies.

FAQ’s

Q1. Who elects the Presiding Officer in the Parliament?

Ans: The Presiding Officer is typically elected by MPs in the House of Parliament.

Q2. Can the Presiding Officer vote in parliamentary proceedings?

Ans: In most cases, the Presiding Officer is not allowed to vote in parliamentary proceedings, except in the case of a tied vote.

Q3. Can the Presiding Officer be removed from office?

Ans: Yes, the Presiding Officer can be removed from office through a vote of no confidence by the MPs in the House of Parliament.

Q4. What is parliamentary privilege?

Ans: Parliamentary privilege is a set of legal immunities and privileges that are granted to MPs in order to protect their ability to carry out their parliamentary duties without interference or obstruction.

Q5. What is a point of order in parliamentary procedure?

Ans: A point of order is a question or objection raised by an MP during parliamentary proceedings relating to the interpretation or application of parliamentary rules or procedures.

Syntax And Examples Of Matlab Syms

Introduction to Matlab Syms

In MATLAB, syms is used as a shortcut to the inbuilt function sym. This function can be used to create symbolic variables. The Symbolic variables used in MATLAB are not constants like the regular variables; we do not assign values to them. One important function in this toolbox is the syms function, which creates a symbolic object and automatically assigns it to a MATLAB variable with the same name. This allows for convenient manipulation and evaluation of symbolic expressions in MATLAB.

Start Your Free Data Science Course

Hadoop, Data Science, Statistics & others

Syntax of Matlab Syms

Syntax for Syms Function in Matlab:

syms variable1 variable2 …... variableN

syms variable1 variable2 …... variabeN [n1 …... nM]

syms f(variable1, variable2, …..., variableN)

Description:

syms variable1 variable2 …… variableN is used to create symbolic variables variable1 … variableN. Separate different variables by spaces. ‘syms’ function will clear all the assumptions from variables.

syms variable1 variable2….. variableN [n1 … nM] is used to create symbolic arrays variable1 …. variable. Each array will have the size n1- X -…- X -nM & will contain automatically generated symbolic variables.

syms f(variable1, vaiable2, ….., variableN)is used to create the symbolic function & symbolic variables, which will represent input arguments of function ‘f.’ Please note that a single call can be used to create more than one symbolic function.

Examples of Matlab Syms

Let us now understand the code to use syms in MATLAB.

Example #1

Code:

syms A

[Creating symbolic variable A using syms]

[Creating symbolic variable A using syms]

A

[Displaying the variable created]

[Displaying the variable created]

The command syms A will create a symbolic variable ‘A’ & will automatically assign it to a MATLAB variable with the same name.

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

Input:

A

Output:

As we can see in the output, the command syms A has created a symbolic variable ‘A’ & assigned it to a variable with the same name (A).

Example #2

In this example, we will use syms function to create multiple variables.

Code:

syms A B C

[Creating symbolic variables, A, B, C using syms] C

[Displaying the variables created]

[Displaying the variables created]

The command syms A B C will create 3 symbolic variables A, B & C & will automatically assign these to MATLAB variables with the same name.

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

Input:

C

Output:

As we can see in the output, the command ‘syms A B C’ has created 3 symbolic variables and assigned them to variables with the same name (A, B, C).

Example #3

Code:

syms x [1 5]

[Creating symbolic vector ‘x’ using syms]

[Creating symbolic vector ‘x’ using syms]

The command syms x [1 5] will create a symbolic vector ‘x’ of the size 1 X 5

x

[Displaying the vector created]

[Displaying the vector created]

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

Input:

syms x [1 5] x

Output:

As we can see in the output, the command syms x [1 5] has created a symbolic vector of the size 1 X 5.

Example #4

In this example, we will use syms function to create a symbolic matrix with multiple rows. This output matrix will have its elements generated automatically in the workspace.

Code:

syms x [2 4]

[Creating symbolic matrix ‘x’ using syms]

[Creating symbolic matrix ‘x’ using syms]

The command syms x [2 4] will create a symbolic matrix ‘x’ of the size 2 X 4

x

[Displaying the matrix created]

[Displaying the matrix created]

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

Input:

syms x [2 4]

Output:

As we can see in the output, the command syms x [2 5] has created a symbolic matrix of the size 2 X 4.

Example #5

In this example, we will use syms function to create a symbolic function with 3 variables x, y, z. Below are the steps we will follow:

Create a symbolic function of required variables/arguments.

Specify the formula for the function created.

Pass the arguments to compute the value of the function.

Code:

syms f(x,y,z)

[Creating symbolic function ‘f’ using syms]

[Creating symbolic function ‘f’ using syms]

f(x,y,z) = 2*x + 5*y - z^2

[Specify the formula for the function created]

[Specify the formula for the function created]

f(1,2,3)

[Pass the arguments to compute the value of the function]

[Pass the arguments to compute the value of the function]

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

Input:

f(1,2,3)

Output:

As we can see in the output, the command syms f (x, y, z) has created a symbolic function ‘f’.

Conclusion

Syms function is used in creating symbolic variables dynamically.

These are used to solve various expressions with the help of functions available in MATLAB.

Syms function can also be used in creating symbolic functions dynamically.

Recommended Articles

This is a guide to Matlab Syms. Here we also discuss the introduction and syntax of Matlab syms, different examples, and code implementation. You may also have a look at the following articles to learn more –

Types And Examples Of Control Activities

Introduction to Control Activities Meaning

The term “control activities” (CA) refers to the policies, procedures, and mechanisms put in place by the management of an organization to reduce the risks identified during the risk assessment process. In short, Control Activities refer to the actions taken by the management to either mitigate or minimize risk.

How Does it Work?

The CA takes place at multiple levels and across all functions of an organization. It is the responsibility of the management to establish effective and efficient control activities, which can be a preventive, detective, or corrective in nature.

Start Your Free Investment Banking Course

Download Corporate Valuation, Investment Banking, Accounting, CFA Calculator & others

Preventive: These types of CA are relatively cost-effective in nature as these are implemented upfront with the intention to prevent the loss of assets in the first place.

Corrective: Once the detective CA identifies the errors or irregularities, then these types of CA are implemented with the sole intention of fixing the issues at hand. In some cases, overhauling of the existing system is required to put in place a new system to prevent the issues.

Types of Control Activities

Authorization: These types of CA are put in place to ensure that all transactions within the organization are carried out according to the limits and exceptions that have been stated in the policy framework or granted by the appropriate officials.

Review & approval: These types of CA are put in place to ensure that the appropriate personnel reviews all transactions for accuracy and completeness.

Verification: These control activities include various computer and manual controls that are put in place to ensure that all accounting information is captured correctly.

Reconciliation: These control activities include validation of accounting information recorded in systems by comparing them with the source chúng tôi helps in ensuring that the financial records are absolutely correct.

Physical security over assets: These types of CA are put in place to ensure that the assets are protected from losses or damages due to negligence, fraud, theft, natural disaster, accident etc.

Segregation of duties: These types of control activities help in reducing the risk of human error, negligence or fraud by involving more than one person in a particular process.

Education, training & coaching: These types of control activities help in reducing the risk of error due to inefficiency in operations by providing proper education and training to the personnel so that they perform their duties commendably. However, it is important to review the education and training programs periodically to ensure that they remain updated as per the current industrial and organizational practices.

Performance planning &evaluation: These types of control activities establish the key performance indicators that the organization can use to identify the unexpected and unusual changes in trends. These changes can be the precursor of something much worse and hence require deeper investigation. The evaluations are usually carried out at multiple levels within the organization or found appropriate by the management.

Examples of Control Activities

Now, let us look at some of the examples to understand how the CA help in an actual organizational set-up.

Example #1 Example #2

A particular company designed some new policies to review and reconcile the accounts receivable to ensure timely detection of the delinquent accounts and planning of appropriate actions. The new policies mandate weekly reconciliation of the accounts receivable recorded in the system to the available receipts by the Accountant. The Assistant Controller should then review the reconciliation. At the end of the month, the Account Receivable Supervisor should age the outstanding receivable balances, which the Assistant Controller should then review. Finally, the delinquent accounts should be taken up for further investigation, while the Controller should approve the written-off bad debt.

Conclusion

Control activities are the actions taken by the management at multiple levels and across all functions of an organization to either mitigate or minimize risk.

There are three major types of CA – Preventive control activities, Detective control activities, and Corrective control activities.

Authorization, review & approval, verification, reconciliation, physical security over assets, segregation of duties, education, training & coaching, and performance planning & evaluation some of the most commonly used CA.

Recommended Articles

This is a guide to Control Activities. Here we also discuss the definition and types of control activities along with how does it works?. You may also have a look at the following articles to learn more –

Update the detailed information about Examples And Functions Of Python Numpy.diff() on the Flu.edu.vn website. We hope the article's content will meet your needs, and we will regularly update the information to provide you with the fastest and most accurate information. Have a great day!