sum = lambda arg1, arg2: arg1 + arg2;
# call sum as a function
print(“Value of total : ” , sum ( 10 , 20 )
print(“Value of total : ” , sum ( 20 , 20 )RUN
>>>
Value of total: 30
Value of total: 40
>>>
The return STATEMENT
A Python function could also optionally return a value. This value could be a result produced from your function’s execution or even be an expression or value that you specify after the keyword ‘return’. And, after a return statement is executed, the program flow goes back to the state next to your function call and gets executed from there. So, to call a Python function at any place in your code, you will only have to use its name and pass arguments in its parentheses, if any. A return statement with no arguments is the same as a return None.
Example Demo of return statement.
# Function definition def sum( arg1, arg2 ): # Add both the parameters and return them. total = arg1 + arg2 print(“Inside the function : “, total) return total # Now you can call sum function total = sum( 10, 20 ) print(“Outside the function : “, total )def add_num(x,y): sum = x + y return sum num1 = 2 num2 = 5 print(“The sum is”. add_num(num1, num2))RUN >>> Inside the function: 30 Outside the function: 30 The sum is 7 >>> |
Let’s Try
def add_num(x,y): sum = x + y return sum num1 = 2 num2 = 5 print(“The sum is”, add_num(num1 , num2)) |
Lifetime and Scope of Variables
A variable’s lifetime is the period of time for which it resides in the memory. The lifetime of variables inside a function is as long as the function executes. They are destroyed once you return from the function. Hence, a function does not remember the value of a variable from its previous calls.
All variables in a program may not be accessible at all locations in that program. This depends on where you have declared a variable. The variable declared in one part of the program may not be accessible to the other parts. The scope of a variable determines the portion of the program where you can access a particular identifier. There are two basic scopes of variables in Python.
Variables that are defined inside a function body have a local scope. In other words, it is local to that function and those defined outside have a global scope. This means that local variables can be accessed only inside the function in which they are declared, If you then try to access the variable x outside the function, it will give NameError.
Global variables can be accessed throughout the program body by all functions. When you call a function, the variables declared inside it are brought into scope, def func1 ( ) : x=9 # Local scope print(x) func1 ( ) >>> 9 >>> y=9 # Global scope def func2(): print(y) func2 ( ) >>> 9 >>> Let us discuss the global scope of a variable in brief. (a) The global names are variables assigned at the top level of the enclosing module file. It means that it is visible everywhere within the program. (b) The global names must be declared only if they are assigned within a function. (c) Global variables must be referenced within a function without being declared. See Example 14.
Example Demo of local and global variables.
total = 0; # This is global variable. # Function definition def sum( arg1, arg2 ): # Add both the parameters and return them. total = arg1 + arg2; # Here total Is local variable . print(“Inside the function local total : “, total) return total # Now you can call sum function sum( 20, 40 ) print(“Outside the function global total : “, total) |
When the above code is executed, it produces following result: Inside the function local total: 60 Outside the function global total: 0
global Statements
The global statements are remotely like declaration statements in Python. They do not type or size declarations, though they are namespace declarations. The global statement tells Python that a function plans to change one or more global names, i.e., names that live in the enclosing module’s scope (namespace).
A namespace is a practical approach to define the scope, and it helps to avoid name conflicts.
void FUNCTION
A function that doesn’t return a value is called a void function or non-fruitful function whereas a function that returns a value is called fruitful function, void functions might display something on the screen or have some other effect, but they don’t have a return value. If you try to assign the result to a variable, you get a special value called None.
A void function internally returns an empty value None.
Library functions
Input ( ) is built-in functions, used to obtain informa-tion from the user.
>>> input ( ” Enter a number : ” ) Enter a number: 57 ‘ 57 ‘ Note that this returns the input as a string. If you want to take 57 as an integer, you need to apply the int() function to it. The int( ) converts a value to an integer.
>>> int(input(“Enter a number”)) Enter a number: 57 57
Example Demo of Input ( ) function.
#Demo of Input 0 function print ( ‘ Please enter some text : ‘ ) x = input ( ) print ( ‘ Text entered is : ‘ , x ) print ( ‘ Type : ‘ , type ( x ) ) print ( ‘ Please enter an integer value : ‘ ) x = input ( ) print ( ‘ Please enter another integer value : ‘ ) y = input ( ) num1 = int(x) num2 = int(y) print(num1, ‘+’, num2, ‘=’, num1 + num2)RUN >>> Please enter some text: Hello Text entered is: Hello Type: <class ‘str’> Please enter an integer value: 4 Please enter another integer value: 2 4 + 2 = 6 >>> |
The eval ( ) function converts a string containing a valid expression to an object. For example, x = 9 print(eval(‘x + 1’)) >>> 10 >>> >>> c = eval ( ” 3 , 5 , 6 ” ) # c = ( 3 , 5 , 6 ) >>> c ( 3 , 5 , 6 ) >>>
The print ( ) function enables a Python program to display textual information to the user, print(“Hello, World!”) print(“I am learning Python”)
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Here you will learn about difference between top-down and bottom-up approach.
Today we are going to have a comparative study of the two approaches being used in field of structured and object oriented programming. We shall start with a brief understanding of the both followed by comparison and conclusion.
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When talking in terms of computer science and programming, the algorithms we use to solve complex problems in a systematic and controlled way are designed on the basis of two approaches that is Top-down and Bottom-up approach. The ideology behind top-down approach is, a bigger problem is divided into some smaller sub-problems called modules, these modules are then solved individually and then integrated together to get the complete solution to the problem. In bottom-up approach on the other hand, the process starts with elementary modules and then combining together to get the desired result. Let us now quickly see in brief what these two approaches has to offer, how they differ from each other and what are the similarities.
The basic idea in top-down approach is to break a complex algorithm or a problem into smaller segments called modules, this process is also called as modularization. The modules are further decomposed until there is no space left for breaking the modules without hampering the originality. The uniqueness of the problem must be retained and preserved. The decomposition of the modules is restricted after achieving a certain level of modularity. The top-down way of solving a program is step-by-step process of breaking down the problem into chunks for organising and solving the sole problem. The C- programming language uses the top-down approach of solving a problem in which the flow of control is in the downward direction.
As the name suggests, this method of solving a problem works exactly opposite of how the top-down approach works. In this approach we start working from the most basic level of problem solving and moving up in conjugation of several parts of the solution to achieve required results. The most fundamental units, modules and sub-modules are designed and solved individually, these units are then integrated together to get a more concrete base to problem solving.
This bottom-up approach works in different phases or layers. Each module designed is tested at fundamental level that means unit testing is done before the integration of the individual modules to get solution. Unit testing is accomplished using low-level functions, that is another topic we will talk about later.
Let us now see a comparative study of both the strategies and try to understand what are common and odds among them.
Divides a problem into smaller units and then solve it. | Starts from solving small modules and adding them up together. |
This approach contains redundant information. | Redundancy can easily be eliminated. |
A well-established communication is not required. | Communication among steps is mandatory. |
The individual modules are thoroughly analysed. | Works on the concept of data-hiding and encapsulation. |
Structured programming languages such as C uses top-down approach. | OOP languages like C++ and Java, etc. uses bottom-up mechanism. |
Relation among modules is not always required. | The modules must be related for better communication and work flow. |
Primarily used in code implementation, test case generation, debugging and module documentation. | Finds use primarily in testing. |
After having a sound discussion on this we all should now have got a clear understanding of the two approaches. The top-down approach is the conventional approach in which decomposition of higher level system into lower level system takes place respectively. Talking about the bottom-up mechanism for algorithm designing, starting from designing lower abstraction modules and then integrating them to higher level provides better efficiency.
We have seen the modules in top-down approach aren’t connected in a manner so that they can communicate well, so giving rise to redundancies, whereas in the later case the redundancies are omitted to large extent. The feature of information hiding and reusability provided by bottom-up approach makes this mechanism even more popular.
Comment below if you have doubts regarding difference between Top-down and Bottom-up approach.
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The bottom-up methodology works best in small groups because it's harder to give every voice equal consideration on larger teams. Use this approach when: The problem's so complex you can't fully understand it at the outset. The problem requires detailed and fundamental knowledge before your team can move on to higher-level work.
The top-down approach to management is when company-wide decisions are made solely by leadership at the top, while the bottom-up approach gives all teams a voice in these types of decisions. Below, we cover the details, pros, and cons of top-down vs. bottom-up management. The top-down approach to management is a strategy in which the decision ...
A top-down approach is a method or strategy of analysis, problem-solving, or organization where the process begins at the highest conceptual level and progresses to the details. This approach often contrasts with the bottom-up approach, which starts with the details and works upwards to form a comprehensive view or solution.
For example, improving a process or solving a problem. Differences Between Top-Down and Bottom-Up Approaches. The main difference between top-down and bottom-up approaches is the direction of the flow of communication and decision-making. In top-down approach, communication and decision-making flow from top to bottom.
Your McKinsey boss or interviewer will prefer that you apply strategic, top-down problem solving over a tactical, bottom-up approach. The top-down approach focuses on the biggest question followed by the critical or vital few drivers of impact first, before dealing with tactical details. The bottom-up approach focuses on the details first, then organizing them into buckets that build up to key ...
The top-down and bottom-up approaches are two methods of problem-solving that help to break down complex tasks. The top-down approach begins by looking at the big picture, defining the overall goals, and breaking them down into smaller parts. On the other hand, the bottom-up approach works in the opposite direction, starting with the components ...
There is no business management approach or even leadership style that fits all. In the complex world of organizational decision-making, two different methods emerged as crucial players: the top-down approach and the bottom-up approach.. These models shape how businesses create their plans, carry out projects, come to agreements, and even set their overall goals.
Top-down is the direct, forward-looking approach. The top-down approach to problem-solving is the most immediate way to initiate. Designers establish goals, identify constraints, execute research, test prototypes, and implement possible solutions to test in the application field. Sometimes there is not enough time and resources to explore the ...
The top-down approach relies on higher authority figures to determine larger goals that will filter down to the tasks of lower level employees. In comparison, the bottom-up style of communication features a decision-making process that gives the entire staff a voice in company goals. Each task remains fluid as employees achieve their goals.
In top-down approach of problem-solving, a problem or information is centralized to have a holistic view or a bigger perspective. Once the general problem is clear, you can move on to more specific details contributing to the main challenge. A top-down approach to problem-solving moves from the macro-level to the micro-level.
Conclusion. For any product team, the top-down and bottom-up approaches are crucial strategies. The top-down approach provides a high-level vision for the product, while the bottom-up approach ensures that the planned features in the roadmap are prioritized and designed to satisfy the market's needs and bridge any gaps.
The bottom-up approach (to dynamic programming) consists in first looking at the "smaller" subproblems, and then solve the larger subproblems using the solution to the smaller problems. The top-down consists in solving the problem in a "natural manner" and check if you have calculated the solution to the subproblem before. I'm a little confused.
Top-down design is a method of breaking a problem down into smaller, less complex pieces from the initial overall problem. Most "good" problems are too complex to solve in just one step, so we divide the problem up into smaller manageable pieces, solve each one of them and then bring everything back together again. The process of making the ...
A top-down approach (also known as stepwise design) is essentially the breaking down of a system to gain insight into the sub-systems that make it up. In a top-down approach an overview of the system is formulated, specifying but not detailing any first-level subsystems. Each subsystem is then refined in yet greater detail, sometimes in many ...
Here are the seven steps of the rational approach: Define the problem. Identify possible causes. Brainstorm options to solve the problem. Select an option. Create an implementation plan. Execute the plan and monitor the results. Evaluate the solution. Read more: Effective Problem Solving Steps in the Workplace.
The working of the top-down approach is very simple. It is done by analyzing the decision and then the major decision is then taken by consulting the other team members as well. The top-down approach can be effective because it remains the same from project to project. The top-down approach is well-practiced and grows more efficient over time.
The following table highlights all the major differences between top-down approach and bottom-up approach −. 1. In this approach, the problem is broken down into smaller parts. In this approach, the smaller problems are solved. 2. 3. It is generally used with documentation of module and debugging code.
4. Okay answer: My approach to problem-solving is to stay calm and reflective. I take a moment to step back, assess the situation objectively, and then determine what actions to take. I try to look at the problem from different angles to find the root cause and develop a strategy to resolve it.
4. In this the communications is less among modules. In this module must have communication. 5. It is used in debugging, module documentation, etc. It is basically used in testing. 6. In top down approach, decomposition takes place. In bottom up approach composition takes place.
Top-down and bottom-up approaches are methods used to analyze and choose securities. However, the terms also appear in many other areas of business, finance, investing, and economics.
1. OVERVIEW. Top down analysis is a problem solving mechanism whereby a given problem is successively broken down into smaller and smaller sub-problems or operations until a set of easily solvable (by computer) sub-problems is arrived at. Each level is numbered commencing with the top (first) level followed by the second level and so on.
In summary, the top-down method is a program design technique that analyses a problem in terms of more elementary subtasks. Through the technique of stepwise splitting, you expand and define each of the separate subtasks until the problem is solved. Each subtask is tested and verified before it is expanded further.
What is problem-solving? Problem-solving is both an ability and a process. As an ability, problem-solving can aid in resolving issues faced in different environments like home, school, abroad, and social situations, among others. As a process, problem-solving involves a series of steps for finding solutions to questions or concerns that arise ...
The top-down way of solving a program is step-by-step process of breaking down the problem into chunks for organising and solving the sole problem. The C- programming language uses the top-down approach of solving a problem in which the flow of control is in the downward direction.
The more focused and accurate you are about the points you write down, the more useful your SWOT analysis will be. Work backwards. Experiment with filling in the four sections of your SWOT analysis in a different order, to stimulate new ways of thinking. Working backwards, in particular, from threats to strengths, may cast new light on the ...