explain problem solving in python

• Computer Vision
• Problem Solving in Python
• Intro to DS and Algo
• Analysis of Algorithm
• Dictionaries
• Linked Lists
• Doubly Linked Lists
• Circular Singly Linked List
• Circular Doubly Linked List
• Tree/Binary Tree
• Binary Search Tree
• Binary Heap
• Sorting Algorithms
• Searching Algorithms
• Single-Source Shortest Path
• Topological Sort
• Dijkstra’s
• Bellman-Ford’s
• All Pair Shortest Path
• Minimum Spanning Tree
• Kruskal & Prim’s

Problem-solving is the process of identifying a problem, creating an algorithm to solve the given problem, and finally implementing the algorithm to develop a computer program .

An algorithm is a process or set of rules to be followed while performing calculations or other problem-solving operations. It is simply a set of steps to accomplish a certain task.

In this article, we will discuss 5 major steps for efficient problem-solving. These steps are:

• Understanding the Problem
• Exploring Examples
• Breaking the Problem Down
• Solving or Simplification
• Looking back and Refactoring

While understanding the problem, we first need to closely examine the language of the question and then proceed further. The following questions can be helpful while understanding the given problem at hand.

• Can the problem be restated in our own words?
• What are the inputs that are needed for the problem?
• What are the outputs that come from the problem?
• Can the outputs be determined from the inputs? In other words, do we have enough information to solve the given problem?
• What should the important pieces of data be labeled?

Example : Write a function that takes two numbers and returns their sum.

• Implement addition
• Integer, Float, etc.

Once we have understood the given problem, we can look up various examples related to it. The examples should cover all situations that can be encountered while the implementation.

• Start with simple examples.
• Progress to more complex examples.
• Explore examples with empty inputs.
• Explore examples with invalid inputs.

Example : Write a function that takes a string as input and returns the count of each character

After exploring examples related to the problem, we need to break down the given problem. Before implementation, we write out the steps that need to be taken to solve the question.

Once we have laid out the steps to solve the problem, we try to find the solution to the question. If the solution cannot be found, try to simplify the problem instead.

The steps to simplify a problem are as follows:

• Find the core difficulty
• Temporarily ignore the difficulty
• Write a simplified solution
• Then incorporate that difficulty

Since we have completed the implementation of the problem, we now look back at the code and refactor it if required. It is an important step to refactor the code so as to improve efficiency.

The following questions can be helpful while looking back at the code and refactoring:

• Can we check the result?
• Can we derive the result differently?
• Can we understand it at a glance?
• Can we use the result or mehtod for some other problem?
• Can you improve the performance of the solution?
• How do other people solve the problem?

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Understanding the Basics of Algorithmic Problems

An algorithm is a step-by-step procedure for solving a problem or accomplishing a specific task. Algorithmic problems are challenges that require an algorithm to solve. These challenges often involve data manipulation or optimization.

Step 1: Understand the Problem

The first step in solving any problem, algorithmic or otherwise, is to understand the problem thoroughly. This involves identifying the problem's inputs, expected outputs, and constraints.

Step 2: Design the Algorithm

Once you understand the problem, the next step is to design an algorithm to solve it. This involves breaking down the problem into smaller, more manageable parts and designing a solution for each.

Example: Solving a Sorting Problem

Let's look at a simple algorithmic problem: sorting a list of numbers in ascending order. Here is a Python function that uses the Bubble Sort algorithm to solve this problem:

The bubble_sort function sorts the list of numbers by repeatedly swapping adjacent numbers if they are in the wrong order.

Step 3: Implement the Algorithm

After designing the algorithm, you must implement it in Python. This involves writing Python code that follows the steps outlined in your algorithm.

The code above creates a list of numbers, sorts it using the bubble_sort function, and then prints the sorted list.

Step 4: Test the Algorithm

After implementing the algorithm, you should test it to ensure it works correctly. This involves running your Python code and checking that it produces the expected output for various input values.

The code snippet above shows the output of running the bubble_sort.py script in a shell. As you can see, it correctly sorts the list of numbers.

Step 5: Analyze the Algorithm

You should analyze your algorithm to understand its efficiency. This involves calculating your algorithm's time and space complexity, which are measures of how the algorithm's resource usage scales with the input size.

For Bubble Sort, the time complexity is O(n^2), and the space complexity is O(1). This means Bubble Sort can be inefficient for large lists but uses a small, constant amount of memory.

In conclusion, solving algorithmic problems in Python involves understanding the problem, designing an algorithm to solve it, implementing it in Python, testing it, and analyzing its efficiency. This process is a fundamental part of Python programming and general software development. If you're looking to hire Python developers who are proficient in solving algorithmic problems, check out Reintech.

If you're interested in enhancing this article or becoming a contributing author, we'd love to hear from you.

Please contact Sasha at [email protected] to discuss the opportunity further or to inquire about adding a direct link to your resource. We welcome your collaboration and contributions!

An algorithm is a step-by-step procedure or set of instructions designed to solve a problem or achieve a specific goal. In computer programming, algorithms are used to perform tasks, manipulate data, and make decisions based on certain conditions. Developing efficient algorithms is a crucial aspect of computer science and software engineering.

To learn more about algorithms, visit the Wikipedia page on the subject.

Bubble Sort

Bubble Sort is a simple comparison-based sorting algorithm. It works by repeatedly swapping adjacent elements if they are in the wrong order. This process continues until no more swaps are needed, indicating that the list is sorted. Although it's straightforward and easy to implement, Bubble Sort is not efficient for large datasets compared to other sorting algorithms like Quick Sort or Merge Sort. More info .

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Computational Thinking

Computational thinking 1 #.

Finding the one definition of Computational Thinking (CT) is a task doomed to failure, mainly because it is hard to have a consensus on what CT means to everyone. To ease the communication and have a shared meaning, we consider CT as an approach to “solve problems using computers as tools” (Beecher, 2017). Notice that CT is not exclusively considered in computer science. On the contrary, CT impacts a wide range of subject areas such as mathematics, engineering, and science in general.

There is a misunderstanding regarding the relationship between CT, computer science, and programming. Some people use them interchangeably, but the three terms are not equivalent. Computer science aims at building and understanding the principles of mathematical computation; while programming –which happens to be a subfield of computer science–focuses on high-quality program writing. Although the three terms are related and might intersect in many points, CT should be seen as a method of problem-solving that is frequently (but not exclusively) used in programming. In essence, problem-solving is a creative process.

Some of the core concepts behind CT are:

Decomposition

Abstraction

Generalization

Hereafter, we will explore what these concepts mean and how they can help you when solving a problem.

Computational thinking

Computational thinking is an approach to problem-solving that involves using a set of practices and principles from computer science to formulate a solution that’s executable by a computer. [ Bee07 ]

Logic is the study of the formal principles of reasoning. These principles determine what can be considered a correct or incorrect argument. An argument is “a change of reasoning that ends up in a conclusion” [ Bee07 ] . Every statement within an argument is called a proposition , and it has a truth value . That is, it is either true or false . Thus, certain expressions like questions (e.g. “are you hungry?”) or commands (e.g. “clean your bedroom!”) cannot be propositions given that they do not have a truth value.

Below we present a very famous argument in the logic field:

Socrates is a man.

All men are mortal.

Therefore, Socrates is mortal.

This argument is composed of three propositions (each one is enumerated). These statements are propositions because all of them have a truth value. However, you can notice that some of these statements are used to arrive at a conclusion. The propositions that form the basis of an argument are known as premises . In our example, propositions 1 and 2 are premises. Lastly, the proposition that is derived from a set of premises is known as a conclusion .

Logic studies the formal principles of reasoning, which determine if an argument is correct or not. An argument is a sequence of premises. Each premise is a statement that has a truth value (i.e. true or false).

Types of Arguments #

Arguments can either be deductive or inductive.

Deductive Arguments #

Deductive arguments are considered a strong form of reasoning because the conclusion is derived from previous premises. However, deductive arguments can fail in two different scenarios:

False premises: One of the premises turns to be false. For example:

I eat healthy food.

Every person eating healthy food is tall.

I am tall. In this example, premise 2 is false. It is not true that every person eating healthy food is tall. Then, this argument fails.

Faulty logic: The conclusion is wrongly derived out from the premises. For example:

All bitterballen are round.

The moon is round.

The moon is a bitterbal. In this case, the conclusion presented in premise 3 is false. It is true that bitterballen are round and that the moon is round, but this does not imply that the moon is a bitterbal! By the way, a “bitterbal” is a typical Dutch snack, to be eaten with a good glass of beer!

Inductive Arguments #

Although deductive arguments are strong, they follow very stern standards that make them difficult to build. Additionally, the real world usually cannot be seen just in black and white–there are many shades in between. This is where inductive arguments play an important role. Inductive arguments do not have an unquestionable truth value. Instead, they deal with probabilities to express the level of confidence in them. Thus, the conclusion of the argument cannot be guaranteed to be true, but you can have a certain confidence in it. An example of an inductive argument comes as follows:

Every time I go out home it is raining.

Tomorrow I will go out from home.

Tomorrow it will be raining.

There is no way you can be certain that tomorrow will rain, but given the evidence, there is a high chance that it will be the case. (The chance might be even higher if we mention that we are living in the Netherlands!)

Deductive and inductive arguments

Arguments can be either deductive or inductive. On the one hand, deductive arguments have premises that have a clear truth value. On the other hand, inductive arguments have premises associated with a level of confidence in them.

Boolean Logic #

Given their binary nature, computers are well suited to deal with deductive reasoning (rather than inductive). To allow them to reason about the correctness of an argument, we use Boolean logic. Boolean logic is a form of logic that deals with deductive arguments–that is, arguments having propositions with a truth value that is either true or false. Boolean logic or Boolean algebra was introduced by George Boole (that is why it is called Boolean) in his book “The Mathematical Analysis of Logic” in 1847. It deals with binary variables (with true or false values) representing propositions and logical operations on them.

Logical Operators #

The main logical operators used in Boolean logic to connect propositions are:

And operator: It is also known as the conjunction operator. It chains premises in such a way that all of them must be true for the conclusion to be true.

Or operator: It is also known as the disjunction operator. It connects premises in such a way that at least one of them must be true for the conclusion to be true.

Not operator: It is also known as the negation operator. It modifies the value of a proposition by flipping its truth value.

In Boolean logic, propositions are usually represented as letters or variables (see previous figure). For instance, going back to our Socrates argument, we can represent the three propositions as follows:

P: Socrates is a man.

Q: All men are mortal.

R: Therefore, Socrates is mortal.

In order for R to be true, P and Q most be true.

We can now translate this reasoning into Python. To do that use the following table and execute the cell below.

The previous translation to Python code of the deductive argument is correct, however we can do way better! Let us see:

Sometimes conditionals are not really needed; using a Boolean expression as the one shown above is sufficient. Having unneeded language constructs (e.g. conditionals) can negatively impact the readability of your code.

Algorithms #

We can use logic to solve everyday life problems. To do so, we rely on the so-called algorithms. Logic and algorithms are not equivalent. On the contrary, an algorithm uses logic to make decisions for it to solve a well-defined problem. In short, an algorithm is a finite sequence of well-defined instructions that are used to provide a solution to a problem. According to this definition, it seems that algorithms are what we create, refine, and execute every single day of our lives, isn’t it? Think for instance about the algorithm to dress up in the morning or the algorithm (or sequence of steps) that you follow to bake a cake or cook a specific recipe. Those are also algorithms that we have internalized and follow unconsciously.

To design an algorithm, we usually first need to specify the following set of elements:

the problem to be solved or question to be answered. If you do not have a well-defined problem you might end up designing a very efficient but useless algorithm that does not solve the problem at hand. Thus, ensure you understand the problem before thinking about a solution;

the inputs of the algorithm–that is, the data you require to compute the solution;

the output of the algorithm–that is, the expected data that you want to get out from your solution, and;

the algorithm or sequence of steps that you will follow to come up with an answer.

Without having a clear picture of your problem, and the expected inputs and output to solve that problem, it will be unlikely that you will be able to come up with a proper algorithm. Furthermore, beware that for algorithms to be useful, they should terminate at some point (they cannot just run forever without providing a solution!) and output a correct result. An algorithm is correct if it outputs the right solution to every possible input. Proving this might be unfeasible, which is why testing your algorithm (maybe after implementing a program) is crucial.

An algorithm is a finite sequence of clearly defined instructions used to solve a specific problem.

Appeltaart Algorithm #

To be conscious about the process of designing an algorithm, let us create an algorithm to prepare the famous Dutch Appeltaart . The recipe has been taken from the Heel Holland Bakt website .

We will start by defining the problem we want to solve. In our case, we want to prepare a classical Dutch Appeltaart .

Inputs (Ingredients) #

The inputs to our algorithm correspond to the ingredients needed to prepare the pie. We list them below:

200 grams of butter

160 grams of brown sugar

0.50 teaspoon of cinnamon

300 grams of white flour

a pinch of salt

breadcrumbs

Apple filling

100 grams of raisins

100 grams of shelled walnuts

5 tablespoons of sugar

3 teaspoons of cinnamon

1 egg for brushing

25 white almonds

Output (Dish) #

The expected output of our algorithm is a delicious and well-prepared Dutch Appeltaart.

Algorithm (Recipe) #

Preparation

Grease a round pan and dust with flour.

Preheat the oven to 180°C.

• Mix the butter with brown sugar and cinnamon.
• Add the flour with a pinch of salt to the butter mixture.
• Add 1 egg to the mixture.
• Knead into a smooth dough and let rest in the fridge for at least 15 minutes.
• Roll out 2/3 of the dough and line the pan with it.
• Spread the breadcrumbs over the bottom.
• Place the pan in the fridge to rest.
• Put the raisins in some rum.
• Peel the apples and cut them into wedges.
• Strain the raisins.
• Mix the apples with raisins, walnuts, sugar, and cinnamon.
• Pour the filling into the pan.
• Cover the top of the pie with the remaining dough.
• Brush the top with a beaten egg and sprinkle with the almonds.
• Bake the apple pie for about 60 minutes.
• Let cool and enjoy!

“The limits of my language mean the limits of my world.” - Ludwig Wittgenstein

In the previous section, we were able to write down the algorithm to prepare a Dutch Appeltaart. Writing down an algorithm is of great importance given that you do not only guarantee that you can execute the algorithm in future opportunities, but you also provide a means to share the algorithm, so other people can benefit from what you designed. But to be able to write down the algorithm, we require an additional tool–that is, a language. In the previous example, we used English to describe our Appeltaart algorithm. However, if you remember correctly, computational thinking relies on computers to solve real-world problems. Thus, the algorithms we want to write down must be written in a language that can be understood by a computer. But beware! The computer is not the only other entity that needs to understand your algorithm: what you write down should be understood by both the machine and other people that might read it afterward (including the you from the future).

Language is a system of communication. It has a syntax and semantics :

The syntax of a language describes the form or structure of the language constructs. The syntax can be defined with a grammar , which is a set of rules that specify how to build a correct sentence in the language.

The semantics describes the meaning of language constructs.

Languages can also be classified as natural or formal :

Natural languages are languages used by people to communicate with each other (e.g. English, Spanish, Dutch, Arabic). They were not designed from the very beginning, on the contrary, they evolved naturally over time.

Formal languages are languages designed by humans for specific domains or purposes. They usually are simplified models of natural language. Examples of formal languages are the mathematical, chemical, or staff (standard music notation) notations. There is also a very important example that interests us the most: programming languages.

Programming Languages #

Programming languages are formal languages with very strict syntax and semantics . These languages are used to write down algorithms. A program is, then, an algorithm implemented using a programming language. To this point, we can make some connections with previous concepts: CT is a method of problem-solving where problems are solved by reusing or designing a new algorithm . This algorithm might later be written down as a program to compute a solution to the original problem.

Python is an example of a programming language. It was conceptualized in 1989 by Guido van Rossum when working at the Centrum Wiskunde & Informatica (CWI), in Amsterdam. Python saw its beginnings in the ABC language. Its name–referring to this monumental sort of snake–actually comes from an old BBC series called Monty Python’s Flying Circus. Guido was reading the scripts of the series and he found out that Python sounded “short, unique, and slightly mysterious”, which is why he decided to adopt the name.

Please clarify that there is a difference between solving a problem, inventing an algorithm, and writing a program.

It maybe the case that to solve a problem you apply a known algorithm, think of sorting words, but it may also be the case that to solve the problem you have to come up with a new algorithm.

The Zen of Python #

In 1999, Tim Peters–one of Python’s main contributors– wrote a collection of 19 principles that influence the design of the Python programming language. This collection is now known as the Zen of Python. Peters mentioned that the 20 \(^{th}\) principle is meant to be filled in by Guido in the years to come. Have a look at the principles. You can consider them as guidelines for the construction of your own programs.

A language is a system of communication, that has syntax and semantics. The syntax describes the form of the language, while the semantics describes its meaning. A language can be either natural (evolves naturally) or formal (designed for a specific domain or purpose). Programming languages are a sort of formal languages and are used to allow communication between humans and computers.

Decomposition #

In 1945 (in the year that the II World War ended), George Pólya, a Hungarian mathematician, wrote a book called “How to Solve It”. The book presents a systematic method of problem-solving that is still relevant today. In particular, he introduced problem-solving techniques known as heuristics . A heuristic is a strategy to solve a specific problem by yielding a good enough solution, but not necessarily an optimal one. There is one important type of heuristic known as decomposition . Decomposition is a divide-and-conquer approach that breaks complex problems into smaller and simpler subproblems, so we can deal with them more efficiently.

In programming, we have diverse constructs that will ease the implementation of decomposition. In particular, classes and functions (or methods ) are a means to divide a complex solution to a complex problem into smaller chunks of code. We will cover these topics later in the course.

Decomposition is a divide-and-conquer approach that breaks complex problems into smaller and simpler subproblems.

Abstraction & Generalization #

Abstraction is the process of hiding irrelevant details in a given context and preserving only the important information. Abstraction is required when generalizing. In fact, generalization is the process of defining general concepts by abstracting common properties from a set of instances. In other words, generalization allows you to formulate a unique concept out of similar but not necessarily identical entities. When generalizing a solution, it becomes simpler because there are fewer concepts to deal with. The solution also becomes more powerful in the sense that it can be applied to more problems of similar nature.

To be able to generalize a solution or algorithm, you need to start developing your ability to recognize patterns. For instance:

A sequence of instructions that are repeating multiple times can be generalized into loops .

Specialized instructions that are employed more than once in different scenarios can be encapsulated in subroutines or functions .

Constraints that determine the flow of execution of your algorithm can be generalized in conditionals .

Abstraction and generalization

Abstraction is the process of preserving relevant information in a given context and hiding irrelevant details. In addition, generalization is the process of defining general concepts by abstracting common properties from a set of instances.

Modelling #

All models are wrong, but some are useful. (Box, 1987)

Humans and computers are not able to completely grasp and understand the real world. Thus, to solve real-world problems, we usually need to create models that help us understand and solve the issue. A model is a representation of an entity. Thus, a model as a representation that, by definition, ignores certain details of the represented entity, is also a type of abstraction . A model in computer science usually consists of:

Entities: core concepts of the represented system.

Relationships: connections among entities within the represented system.

Models usually come in two flavors:

Static models: these models reflect a snapshot of the system at a given moment in time. The state of the system might change in the future but the changes are so small or unfrequent that the model is still useful and sufficient.

Dynamic models: these models explain changes in the state of a system over time. They always involve states (description of the system at a given point in time) and transitions (state changes). Some dynamic models can be executed to predict the state of the system in the future.

When creating a model you should verify that:

The model is useful to solve the problem at hand.

You are strictly following the rules , notation, and conventions to define a model.

The level of accuracy of your model is acceptable to solve the problem (i.e. you keep the strictly needed information).

You have defined the level of precision at which you represent your model (especially when dealing with decimal numbers; how many decimal points are required?).

A model is a representation of an entity. It is a type of abstraction.

Example: Alice’s Adventures in Wonderland #

In this section, we will apply computational thinking to solve a problem from Alice’s Adventures in Wonderland, written by Lewis Caroll. In chapter I, “Down the Rabbit Hole”, Alice found herself falling down into a rabbit hole. She finally reached the ground, coming upon “a heap of sticks and dry leaves”. Alice found herself in a “long, low hall, which was lit up by a row of lamps hanging from the roof”.

“There were doors all around the hall, but they were all locked”. She started “wondering how she was ever to get out again”. She suddenly discovered a tiny golden key on a little three-legged table, all made of solid glass. After looking around, “she came upon a low curtain she had not noticed before, and behind it was a little door about fifteen inches high: she tried the little golden key in the lock, and to her great delight it fitted!” But she was not able to go through it; the doorway was too small.

Later in the chapter, we find out that there was a bottle with a ‘DRINK ME’ label that makes shorter anyone drinking its content. There was also a very small cake with the words ‘EAT ME’ beautifully marked in currant. Anyone eating the cake will grow larger. How can we help Alice go out of the hall through the small doorway behind the curtain?

To solve this problem, assume that:

Alice is 60 inches tall;

Alice can go out of the doorway if she is taller than 10 inches (exclusive) and shorter than 15 inches (inclusive);

one sip of the ‘DRINK ME’ bottle makes anyone get 11 inches shorter;

Alice can only sip from the bottle at a point in time;

one bite of the ‘EAT ME’ cake makes anyone grow 1 inch larger;

Alice can only have a bite of the cake at a point in time, and;

Alice cannot sip from the bottle and a bite of the cake at the same time.

Understanding the Problem #

Problem: Alice wants to go out of the hall. To do so, she needs to go through the doorway behind the curtain, but she is too tall to go through it.

Identifying the Inputs #

The inputs we have at hand are:

Alice’s height (i.e. 60 inches)

Doorway height (i.e. 15 inches)

Height decrease after taking a sip from the ‘DRINK ME’ bottle (i.e. 11 inches)

Height increase after taking a bite from the ‘EAT ME’ cake (i.e. 1 inch)

We abstracted the relevant information to the problem. Details are removed if they are not needed;

thus, the inputs are useful to solve the problem.

The inputs were written in Python. They follow the syntax rules of the programming language.

The identified inputs are enough to accurately solve the problem.

We use integers to represent Alice’s height. There is no need to use floats. Therefore, the level of precision of our model is acceptable to solve the problem.

Identifying the Expected Output #

As a solution to our problem, we want Alice to go out from the hall through the doorway. To do so, her height should be greater than 10 inches and less than or equal to 15 inches.

Output: Alice’s height should be greater than 10 inches and less than or equal to 15 inches.

Designing the Algorithm #

To solve the problem we know that:

Alice must go out from the hall through the doorway if she is larger than 10 inches and shorter or exactly 15 inches.

Alice must drink out from the ‘DRINK ME’ bottle if she is taller than 15 inches–that is, she will shorten 11 inches.

Alice must bite the ‘EAT ME’ cake if she is shorter or exactly 10 inches–that is, she will grow 1 inch.

Steps 2 and 3 shall be repeated until she can to go out from the hall.

We decomposed the problem of getting the right height into two subproblems: making Alice get larger and making Alice get shorter.

We generalized our solution by identifying patterns in our algorithm (e.g. step 4).

Now, we will implement our algorithm in Python.

Let us try to improve our previous algorithm. We can always make it more beautiful , more explicit , simpler , and more readable . Remember that “if the implementation is easy to explain, it may be a good idea” (see the Zen of Python).

Note: In the following code, we will use f-Strings. We will talk more about later in the course, but now you can start grasping what is their use and how we can build them.

Well done! We have applied computational thinking to design a solution to Alice’s problem.

(c) 2021, Lina Ochoa Venegas, Eindhoven University of Technology

This Jupyter Notebook is based on Chapter 1 of [ Bee07 ] and Chapter 1, 8, and 10 of [ Erw17 ] .

Problem Solving with Python

8. functions and modules ¶, 8.1. introduction ¶.

In computer programming, functions are a way to bundle multiple lines of code together to run as one block of code. Many functions accept input, called arguments, and produce output. Python has many built-in functions such as type() , len() and pow() . In this chapter you will learn how to create user-defined functions in Python.

By the end of this chapter you will be able to:

Call functions in Python

Import functions into Python scripts

Create user-defined functions

Create functions with default arguments

Utilize functions with positional and keyword arguments

Write reusable code for other problem solvers to use

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8.2. Why Functions?

Say "Hello, World!" With Python Easy Max Score: 5 Success Rate: 96.28%

Python if-else easy python (basic) max score: 10 success rate: 89.75%, arithmetic operators easy python (basic) max score: 10 success rate: 97.43%, python: division easy python (basic) max score: 10 success rate: 98.68%, loops easy python (basic) max score: 10 success rate: 98.12%, write a function medium python (basic) max score: 10 success rate: 90.31%, print function easy python (basic) max score: 20 success rate: 97.25%, list comprehensions easy python (basic) max score: 10 success rate: 97.70%, find the runner-up score easy python (basic) max score: 10 success rate: 94.14%, nested lists easy python (basic) max score: 10 success rate: 91.65%.

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Understanding Algorithms: The Key to Problem-Solving Mastery

The world of computer science is a fascinating realm, where intricate concepts and technologies continuously shape the way we interact with machines. Among the vast array of ideas and principles, few are as fundamental and essential as algorithms. These powerful tools serve as the building blocks of computation, enabling computers to solve problems, make decisions, and process vast amounts of data efficiently.

An algorithm can be thought of as a step-by-step procedure or a set of instructions designed to solve a specific problem or accomplish a particular task. It represents a systematic approach to finding solutions and provides a structured way to tackle complex computational challenges. Algorithms are at the heart of various applications, from simple calculations to sophisticated machine learning models and complex data analysis.

Understanding algorithms and their inner workings is crucial for anyone interested in computer science. They serve as the backbone of software development, powering the creation of innovative applications across numerous domains. By comprehending the concept of algorithms, aspiring computer science enthusiasts gain a powerful toolset to approach problem-solving and gain insight into the efficiency and performance of different computational methods.

In this article, we aim to provide a clear and accessible introduction to algorithms, focusing on their importance in problem-solving and exploring common types such as searching, sorting, and recursion. By delving into these topics, readers will gain a solid foundation in algorithmic thinking and discover the underlying principles that drive the functioning of modern computing systems. Whether you’re a beginner in the world of computer science or seeking to deepen your understanding, this article will equip you with the knowledge to navigate the fascinating world of algorithms.

What are Algorithms?

At its core, an algorithm is a systematic, step-by-step procedure or set of rules designed to solve a problem or perform a specific task. It provides clear instructions that, when followed meticulously, lead to the desired outcome.

Consider an algorithm to be akin to a recipe for your favorite dish. When you decide to cook, the recipe is your go-to guide. It lists out the ingredients you need, their exact quantities, and a detailed, step-by-step explanation of the process, from how to prepare the ingredients to how to mix them, and finally, the cooking process. It even provides an order for adding the ingredients and specific times for cooking to ensure the dish turns out perfect.

In the same vein, an algorithm, within the realm of computer science, provides an explicit series of instructions to accomplish a goal. This could be a simple goal like sorting a list of numbers in ascending order, a more complex task such as searching for a specific data point in a massive dataset, or even a highly complicated task like determining the shortest path between two points on a map (think Google Maps). No matter the complexity of the problem at hand, there’s always an algorithm working tirelessly behind the scenes to solve it.

Furthermore, algorithms aren’t limited to specific programming languages. They are universal and can be implemented in any language. This is why understanding the fundamental concept of algorithms can empower you to solve problems across various programming languages.

The Importance of Algorithms

Algorithms are indisputably the backbone of all computational operations. They’re a fundamental part of the digital world that we interact with daily. When you search for something on the web, an algorithm is tirelessly working behind the scenes to sift through millions, possibly billions, of web pages to bring you the most relevant results. When you use a GPS to find the fastest route to a location, an algorithm is computing all possible paths, factoring in variables like traffic and road conditions, to provide you the optimal route.

Consider the world of social media, where algorithms curate personalized feeds based on our previous interactions, or in streaming platforms where they recommend shows and movies based on our viewing habits. Every click, every like, every search, and every interaction is processed by algorithms to serve you a seamless digital experience.

In the realm of computer science and beyond, everything revolves around problem-solving, and algorithms are our most reliable problem-solving tools. They provide a structured approach to problem-solving, breaking down complex problems into manageable steps and ensuring that every eventuality is accounted for.

Moreover, an algorithm’s efficiency is not just a matter of preference but a necessity. Given that computers have finite resources — time, memory, and computational power — the algorithms we use need to be optimized to make the best possible use of these resources. Efficient algorithms are the ones that can perform tasks more quickly, using less memory, and provide solutions to complex problems that might be infeasible with less efficient alternatives.

In the context of massive datasets (the likes of which are common in our data-driven world), the difference between a poorly designed algorithm and an efficient one could be the difference between a solution that takes years to compute and one that takes mere seconds. Therefore, understanding, designing, and implementing efficient algorithms is a critical skill for any computer scientist or software engineer.

Hence, as a computer science beginner, you are starting a journey where algorithms will be your best allies — universal keys capable of unlocking solutions to a myriad of problems, big or small.

Common Types of Algorithms: Searching and Sorting

Two of the most ubiquitous types of algorithms that beginners often encounter are searching and sorting algorithms.

Searching algorithms are designed to retrieve specific information from a data structure, like an array or a database. A simple example is the linear search, which works by checking each element in the array until it finds the one it’s looking for. Although easy to understand, this method isn’t efficient for large datasets, which is where more complex algorithms like binary search come in.

Binary search, on the other hand, is like looking up a word in the dictionary. Instead of checking each word from beginning to end, you open the dictionary in the middle and see if the word you’re looking for should be on the left or right side, thereby reducing the search space by half with each step.

Sorting algorithms, meanwhile, are designed to arrange elements in a particular order. A simple sorting algorithm is bubble sort, which works by repeatedly swapping adjacent elements if they’re in the wrong order. Again, while straightforward, it’s not efficient for larger datasets. More advanced sorting algorithms, such as quicksort or mergesort, have been designed to sort large data collections more efficiently.

Diving Deeper: Graph and Dynamic Programming Algorithms

Building upon our understanding of searching and sorting algorithms, let’s delve into two other families of algorithms often encountered in computer science: graph algorithms and dynamic programming algorithms.

A graph is a mathematical structure that models the relationship between pairs of objects. Graphs consist of vertices (or nodes) and edges (where each edge connects a pair of vertices). Graphs are commonly used to represent real-world systems such as social networks, web pages, biological networks, and more.

Graph algorithms are designed to solve problems centered around these structures. Some common graph algorithms include:

Dynamic programming is a powerful method used in optimization problems, where the main problem is broken down into simpler, overlapping subproblems. The solutions to these subproblems are stored and reused to build up the solution to the main problem, saving computational effort.

Here are two common dynamic programming problems:

Understanding these algorithm families — searching, sorting, graph, and dynamic programming algorithms — not only equips you with powerful tools to solve a variety of complex problems but also serves as a springboard to dive deeper into the rich ocean of algorithms and computer science.

Recursion: A Powerful Technique

While searching and sorting represent specific problem domains, recursion is a broad technique used in a wide range of algorithms. Recursion involves breaking down a problem into smaller, more manageable parts, and a function calling itself to solve these smaller parts.

To visualize recursion, consider the task of calculating factorial of a number. The factorial of a number n (denoted as n! ) is the product of all positive integers less than or equal to n . For instance, the factorial of 5 ( 5! ) is 5 x 4 x 3 x 2 x 1 = 120 . A recursive algorithm for finding factorial of n would involve multiplying n by the factorial of n-1 . The function keeps calling itself with a smaller value of n each time until it reaches a point where n is equal to 1, at which point it starts returning values back up the chain.

Algorithms are truly the heart of computer science, transforming raw data into valuable information and insight. Understanding their functionality and purpose is key to progressing in your computer science journey. As you continue your exploration, remember that each algorithm you encounter, no matter how complex it may seem, is simply a step-by-step procedure to solve a problem.

We’ve just scratched the surface of the fascinating world of algorithms. With time, patience, and practice, you will learn to create your own algorithms and start solving problems with confidence and efficiency.

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Solving Assignment Problem using Linear Programming in Python

Learn how to use Python PuLP to solve Assignment problems using Linear Programming.

In earlier articles, we have seen various applications of Linear programming such as transportation, transshipment problem, Cargo Loading problem, and shift-scheduling problem. Now In this tutorial, we will focus on another model that comes under the class of linear programming model known as the Assignment problem. Its objective function is similar to transportation problems. Here we minimize the objective function time or cost of manufacturing the products by allocating one job to one machine.

If we want to solve the maximization problem assignment problem then we subtract all the elements of the matrix from the highest element in the matrix or multiply the entire matrix by –1 and continue with the procedure. For solving the assignment problem, we use the Assignment technique or Hungarian method, or Flood’s technique.

The transportation problem is a special case of the linear programming model and the assignment problem is a special case of transportation problem, therefore it is also a special case of the linear programming problem.

In this tutorial, we are going to cover the following topics:

Assignment Problem

A problem that requires pairing two sets of items given a set of paired costs or profit in such a way that the total cost of the pairings is minimized or maximized. The assignment problem is a special case of linear programming.

For example, an operation manager needs to assign four jobs to four machines. The project manager needs to assign four projects to four staff members. Similarly, the marketing manager needs to assign the 4 salespersons to 4 territories. The manager’s goal is to minimize the total time or cost.

Problem Formulation

A manager has prepared a table that shows the cost of performing each of four jobs by each of four employees. The manager has stated his goal is to develop a set of job assignments that will minimize the total cost of getting all 4 jobs.  

Initialize LP Model

In this step, we will import all the classes and functions of pulp module and create a Minimization LP problem using LpProblem class.

Define Decision Variable

In this step, we will define the decision variables. In our problem, we have two variable lists: workers and jobs. Let’s create them using  LpVariable.dicts()  class.  LpVariable.dicts()  used with Python’s list comprehension.  LpVariable.dicts()  will take the following four values:

• First, prefix name of what this variable represents.
• Second is the list of all the variables.
• Third is the lower bound on this variable.
• Fourth variable is the upper bound.
• Fourth is essentially the type of data (discrete or continuous). The options for the fourth parameter are  LpContinuous  or  LpInteger .

Let’s first create a list route for the route between warehouse and project site and create the decision variables using LpVariable.dicts() the method.

Define Objective Function

In this step, we will define the minimum objective function by adding it to the LpProblem  object. lpSum(vector)is used here to define multiple linear expressions. It also used list comprehension to add multiple variables.

Define the Constraints

Here, we are adding two types of constraints: Each job can be assigned to only one employee constraint and Each employee can be assigned to only one job. We have added the 2 constraints defined in the problem by adding them to the LpProblem  object.

Solve Model

In this step, we will solve the LP problem by calling solve() method. We can print the final value by using the following for loop.

From the above results, we can infer that Worker-1 will be assigned to Job-1, Worker-2 will be assigned to job-3, Worker-3 will be assigned to Job-2, and Worker-4 will assign with job-4.

In this article, we have learned about Assignment problems, Problem Formulation, and implementation using the python PuLp library. We have solved the Assignment problem using a Linear programming problem in Python. Of course, this is just a simple case study, we can add more constraints to it and make it more complicated. You can also run other case studies on Cargo Loading problems , Staff scheduling problems . In upcoming articles, we will write more on different optimization problems such as transshipment problem, balanced diet problem. You can revise the basics of mathematical concepts in  this article  and learn about Linear Programming  in this article .

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100 Python Code Snippets for Everyday Problems

If you’ve been following me for any amount of time, you know that I regularly publish Python code snippets for everyday problems . Well, I figured I’d finally aggregate all those responses in one massive article with links to all those resources.

Table of Contents

Code snippet repository.

Personally, it’s too much for me to maintain, but I welcome you to help it grow. In the meantime, I’ll continue to update this article. Otherwise, let’s get to the list!

Everyday Problems

In this section, we’ll take a look at various common scenarios that arise and how to solve them with Python code. Specifically, I’ll share a brief explanation of the problem with a list of Python code solutions. Then, I’ll link all the resources I have.

To help you navigate this article, I’ve created separate sections for each type of problem you might find yourself tackling. For instance, I’ve put together a section on strings and a section on lists. In addition, I’ve sorted those sections alphabetically. Within each section, I’ve sorted the problems by perceived complexity. In other words, problems that I believe are more straightforward come first.

Hope that helps keep things organized for you!

Dictionaries (17 Snippets)

One of favorite data structures in Python is the dictionary which maps pairs of items. For example, we might use a dictionary to count the number of words that appear in this article. Each key in the dictionary would be a unique word in this article. Then, each word would be mapped to its count. As you can probably imagine, this kind of structure is very useful, but it comes with its quirks. Let’s take a look at a few!

Merging Two Dictionaries

In this collection, we talk a lot about handling data structures like lists and dictionaries. Well, this one is no different. In particular, we’re looking at merging two dictionaries. Of course, combining two dictionaries comes with risks. For example, what if there are duplicate keys? Luckily, we have solutions for that:

If you’re interested, I have an article which covers this exact topic called “ How to Merge Two Dictionaries in Python ” which features four solutions as well performance metrics.

Inverting a Dictionary

Sometimes when we have a dictionary, we want to be able to flip its keys and values. Of course, there are concerns like “how do we deal with duplicate values?” and “what if the values aren’t hashable?” That said, in the simple case, there are a few solutions:

Performing a Reverse Dictionary Lookup

Earlier we talked about reversing a dictionary which is fine in some circumstances. Of course, if our dictionary is enormous, it might not make sense to outright flip the dict. Instead, we can lookup a key based on a value:

If this seems helpful, you can check out the source article titled “ How to Perform a Reverse Dictionary Lookup in Python “. One of the things I loved about writing this article was learning about generator expressions. If you’re seeing them for the first time, you might want to check it out.

Input/Output (12 Snippets)

In software development, Input/Output (I/O) refers to any time a program reaches for data that is external to the source code. Common examples of I/O include reading from and writing to databases, files, and command line interfaces. Naturally, Python does a great job of making I/O accessible, but there are still challenges. Here are a few!

Printing on the Same Line

Along a similar line as formatting strings, sometimes you just need to print on the same line in Python. As the `print`python command is currently designed, it automatically applies a newline to the end of your string. Luckily, there are a few ways around that:

As always, if you plan to use any of these solutions, check out the article titled “ How to Print on the Same Line in Python ” for additional use cases and caveats.

Making a Python Script Shortcut

Sometimes when you create a script, you want to be able to run it conveniently at the click of a button. Fortunately, there are several ways to do that.

First, we can create a Windows shortcut with the following settings:

Likewise, we can also create a batch file with the following code:

Finally, we can create a bash script with the following code:

If you’re looking for more explanation, check out the article titled “ How to Make a Python Script Shortcut with Arguments .”

Checking if a File Exists

One of the amazing perks of Python is how easy it is to manage files. Unlike Java, Python has a built-in syntax for file reading and writing. As a result, checking if a file exists is a rather brief task:

As always, you can learn more about these solutions in my article titled “ How to Check if a File Exists in Python ” which features three solutions and performances metrics.

Parsing a Spreadsheet

One of the more interesting use cases for Python is data science. Unfortunately, however, that means handling a lot of raw data in various formats like text files and spreadsheets. Luckily, Python has plenty of built-in utilities for reading different file formats. For example, we can parse a spreadsheet with ease:

In this case, we try to get our output in a list of dictionaries. If you want to know more about how this works, check out the complete article titled “ How to Parse a Spreadsheet in Python .”

Lists (43 Snippets)

When it comes to data structures , none are more ubiquitous than the list. In Python, in particular, the list is a dynamic array which uses zero-based indexing. In other words, we can added and remove items without really caring too much about what it looks like under the hood. That makes lists really intuitive. Of course, like other data structures in this list (no pun intended), lists come with their own challenges. Let’s take a look!

Adding An Item to a List

As this collection expanded, I grew interested in Python fundamentals. In other words, what are some things that absolute beginners might want to do, and how many different ways are there to do those things? One of those things was adding an item to a list.

Luckily, Python has a ton of ways of adding items to lists. For example, there’s the popular `append()`python method. However, there are tons of other options. Here are five:

Naturally, I’ve written all about these solutions in more in my article titled “ How to Add an Item to a List in Python .”

Retrieving the Last Item of a List

Since we’re on the topic of lists, lets talk about getting the last item of a list. In most languages, this involves some convoluted mathematical expression involving the length of the list. What if I told you there is are several more interesting solutions in Python?

Checking if a List Is Empty

If you come from a statically typed language like Java or C, you might be bothered by the lack of static types in Python. Sure, not knowing the type of a variable can sometimes be frustrating, but there are perks as well. For instance, we can check if a list is empty by its type flexibility—among other methods:

Cloning a List

One of my favorite subjects in programming is copying data types. After all, it’s never easy in this reference-based world we live, and that’s true for Python as well. Luckily, if we want to copy a list, there are a few ways to do it:

When it comes to cloning, it’s important to be aware of the difference between shallow and deep copies. Luckily, I have an article covering that topic .

Writing a List Comprehension

One of my favorite Python topics to chat about is list comprehensions. As someone who grew up on languages like Java, C/C++, and C#, I had never seen anything quite like a list comprehension until I played with Python. Now, I’m positively obsessed with them. As a result, I put together an entire list of examples:

Summing Elements of Two Lists

Let’s say you have two lists, and you want to merge them together into a single list by element. In other words, you want to add the first element of the first list to the first element of the second list and store the result in a new list. Well, there are several ways to do that:

Converting Two Lists Into a Dictionary

Previously, we talked about summing two lists in Python. As it turns out, there’s a lot we can do with two lists. For example, we could try mapping one onto the other to create a dictionary.

As with many of these problems, there are a few concerns. For instance, what if the two lists aren’t the same size? Likewise, what if the keys aren’t unique or hashable? That said, in the simple case, there are some straightforward solutions:

Sorting a List of Strings

Sorting is a common task that you’re expected to know how to implement in Computer Science. Despite the intense focus on sorting algorithms in most curriculum, no one really tells you how complicated sorting can actually get. For instance, sorting numbers is straightforward, but what about sorting strings? How do we decide a proper ordering? Fortunately, there are a lot of options in Python:

If you’re curious about how some of these solutions work, or you just want to know what some of the potential risks are, check out my article titled “ How to Sort a List of Strings in Python .”

Sorting a List of Dictionaries

Once you have a list of dictionaries, you might want to organize them in some specific order. For example, if the dictionaries have a key for date, we can try sorting them in chronological order. Luckily, sorting is another relatively painless task:

All these solutions and more outlined in my article titled “ How to Sort a List of Dictionaries in Python .”

Meta (6 Snippets)

Sometimes coding is less about writing code and more about making sense of the code. As a result, I felt it made sense to create a section dedicated to solving Python development challenges like testing. Check it out!

Commenting Code

When it comes to writing code, I’m often of the opinion that code should be as readable as possible without comments . That said, comments have value, so it’s important to know how to write them. Luckily, Python supports three main options:

If you’re interested in exploring these options a bit deeper, check out my article titled “ How to Comment Code in Python .”

Testing Performance

Sometimes you just want to compare a couple chunks of code. Luckily, Python has a few straightforward options including two libraries, `timeit`python and `cProfile`python. Take a look:

If you’ve read any of the articles in the How to Python series, then you know how often I use the `timeit`python library to measure performance. That said, it’s nice to know that there are different options for different scenarios.

As always, if you want to learn more about testing, check the article titled “ How to Performance Test Python Code .”

Strings (22 Snippets)

In the world of programming, strings are an abstraction created to represent a collection of characters. Naturally, they can be used to store text data like names and email addresses. Unfortunately, that means strings are extremely complex, so there are a ton of string related problems. In this section, we’ll look at a handful of those problems.

Comparing Strings

Perhaps one of the most common questions people ask after creating a few strings is how to compare them. In Python, there are a lot of different ways to compare strings which depend on your needs. For example, do we want to know if two strings are equal? Or, do we just need to know how they compare alphabetically?

For each scenario, there are different tools we can use. Here’s a quick list of options:

In these examples, we demonstrate a few different types of comparison. For example, we can check for equality using the `==`python operator. Of course, if we only need to check alphabetical ordering, we can opt for one of the relational operators like greater than (`>`python). Likewise, Python has the `is`python operator for checking identity.

Checking for Substrings

One thing I find myself searching more often than I should is the way to check if a string contains a substring in Python. Unlike most programming languages , Python leverages a nice keyword for this problem. Of course, there are also method-based solutions as well:

If you’re like me and forget about the `in`python keyword, you might want to bookmark the “ How to Check if a String Contains a Substring ” article.

Formatting a String

Whether we like to admit it or not, we often find ourselves burying print statements throughout our code for quick debugging purposes. After all, a well placed print statement can save you a lot of time. Unfortunately, it’s not always easy or convenient to actually display what we want. Luckily, Python has a lot of formatting options:

Keep in mind that these solutions don’t have to be used with print statements. In other words, feel free to use solutions like f-strings wherever you need them.

Converting a String to Lowercase

In the process of formatting or comparing a string, we may find that one way to reduce a strings complexity is to convert all the characters to lowercase. For instance, we might do this when we want check if two strings match, but we don’t care if the casing is the same. Here are a few ways to do that:

Like many problems in this collection, there’s an article that goes into even more depth on how to solve this problem; it’s titled “ How to Convert a String to Lowercase in Python ,” and it covers all these solutions and more. In addition, it includes a challenge for converting a string to title case.

Splitting a String by Whitespace

While dealing with locale and other language issues is tough, it’s also tough to handle grammar concepts like words and sentences. For instance, how would we go about breaking a string into words? One rough way to do that is to split that string by spaces. Take a look:

Clearly, the idea of string splitting is a complex subject. If you’re interested in learning more about what went into these snippets, check out the article titled “ How to Split a String by Whitespace in Python .”

Share Your Own Problems

As you can see, this article and its associated series is already quite large. That said, I’d love to continue growing them. As a result, you should consider sharing some of your own problems. After all, there has be something you Google regularly. Why not share it with us?

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Likewise, here are a few Python resources from Amazon (ad):

Otherwise, thanks for stopping by! I appreciate the support.

The How to Python tutorial series strays from the usual in-depth coding articles by exploring byte-sized problems in Python. In this series, students will dive into unique topics such as How to Invert a Dictionary , How to Sum Elements of Two Lists , and How to Check if a File Exists .

Each problem is explored from the naive approach to the ideal solution. Occasionally, there’ll be some just-for-fun solutions too. At the end of every article, you’ll find a recap full of code snippets for your own use. Don’t be afraid to take what you need!

If you have a problem of your own, feel free to ask. Someone else probably has the same problem. Enjoy How to Python!

Jeremy Grifski

Jeremy grew up in a small town where he enjoyed playing soccer and video games, practicing taekwondo, and trading Pokémon cards. Once out of the nest, he pursued a Bachelors in Computer Engineering with a minor in Game Design. After college, he spent about two years writing software for a major engineering company. Then, he earned a master's in Computer Science and Engineering. Today, he pursues a PhD in Engineering Education in order to ultimately land a teaching gig. In his spare time, Jeremy enjoys spending time with his wife, playing Overwatch and Phantasy Star Online 2, practicing trombone, watching Penguins hockey, and traveling the world.

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Why Python?

You might be wondering "Why should I solve problems with Python?" There are other programming languages in the world such as MATLAB, LabView, C++ and Java. What makes Python useful for solving problems?

Python is a powerful programming language

Python defines the types of objects you build into your code. Unlike some other languages such as C, you do not need to declare the object type. The object type is also mutable, you can change the type of object easily and on the fly. There is a wide array of object types built into Python. Objects can change in size. Python objects can also contain mixed data types. Strings and floating point numbers can be part of the same list.

Python has an extensive Standard Library. A huge number of object types, functions and methods are available for use without importing any external modules. These include math functions, list methods, and calls to a computer's system. There is a lot that can be done with the Python Standard Library. The first couple of chapters of this book will just use the standard library. It can do a lot.

Python has over 100,000 external packages available for download and use. They are easy to install off of the Python Package Index, commonly called PyPI ("pie pee eye"). There is a Python package for just about everything. There are packages which can help you: interact with the web, make complex computations, calculate unit conversions, plot data, work with .csv, .xls, and .pdf files, manipulate images and video, read data from sensors and test equipment, train machine learning algorithms, design web apps, work with GIS data, work with astronautical data. There are and many more Python packages added to PyPI every day. In this book, we will use some of the more useful Python packages for problem solvers such as NumPy, Matplotlib, and SymPy.

Python is easy to learn and use

One way Problem solvers code solutions faster in Python faster than coding solutions in other programming languages is that Python is easy to learn and use. Python programs tend to be shorter and quicker to write than a program which completes a similar function in another languages. In the rapid design, prototype, test, iterate cycle programming solutions in Python can be written and tested quickly. Python is also an easy language for fellow problem solvers on your team to learn. Python's language syntax is also quite human readable. While programmers can become preoccupied with a program's runtime, it is development time that takes the longest.

Python is transportable

Python can be installed and run on each of the three major operating systems: Windows, Mac and Linux. On Mac and Linux Python comes installed out of the box. Just open up a terminal in on a MacOS or Linux machine and type python . That's it, you are now using Python. On Windows, I recommend downloading and installing the Anaconda distribution of Python. The Anaconda distribution of Python is free and can be installed on all three major operating systems.

Python is free

Some computer languages used for problem solving such as MATLAB and LabView cost money to download and install. Python is free to download and use. Python is also open source and individuals are free to modify, contribute to, and propose improvements to Python. All of the packages available on the Python Package Index are free to download and install. Many more packages, scripts and utilities can be found in open source code repositories on GitHub and BitBucket.

Python is growing

Python is growing in popularity. Python is particularly growing in the data sciences and in use with GIS systems, physical modeling, machine learning and computer vision. These are growing team problem-solving areas for engineers.

Problem solving Aspects and Python Programming

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Solving Common Python Programming Problems: Tips and Tricks

Sure, I’ll get started on crafting a humorous and fun blog post on solving common Python programming problems based on the provided outlines. Let’s dive into the world of Python programming with a touch of humor! 🐍✨

Identifying and Handling Common Python Programming Problems

Python programming can sometimes feel like herding cats 🐱‍💻—you think you’ve got everything under control until a wild syntax error jumps out of nowhere! 🙀 In this blog post, we will explore some of the most common Python programming problems and equip you with tips and tricks to tackle them head-on. So, grab your coding cape and let’s dive into the wacky world of Python problem-solving! 🦸‍♀️🚀

Syntax Errors

Understanding common syntax mistakes.

Picture this: you’re cruising through your Python code , feeling like a coding wizard 🧙‍♂️, when suddenly, a syntax error slaps you in the face! It’s like a sneaky ninja waiting to trip you up. From missing colons to unmatched parentheses, syntax errors can turn your code from hero to zero in seconds. But fear not, fellow coder! We’ve all been there, and with a few tricks up your sleeve, you’ll be dodging syntax errors like Neo dodges bullets! 💥💻

Let’s uncover the mysteries behind common syntax mistakes and how to outsmart them:

• Tip: Always double-check your indentation and sprinkle those colons like confetti! 🎉
• Tip: Pair up those parentheses and brackets like a match made in coding heaven! 💑

Debugging Techniques for Syntax Errors

Now, when it comes to debugging syntax errors, it’s all about channeling your inner detective 🕵️‍♀️. Sherlock your way through the code, follow the clues, and before you know it, you’ll be cracking the case of the elusive syntax bug! 🔍🐞 Here are some nifty techniques to up your debugging game:

• Print statements are your best friends! Sprinkle them like breadcrumbs through your code to track the elusive bug.
• Use an integrated development environment (IDE) with built-in syntax highlighting and error checking to catch those pesky mistakes before they catch you.

Logical Errors

Identifying logical flaws in code.

Logical errors are the mischievous pranksters of programming 🃏. Your code runs without a hiccup, but the output is as nonsensical as a unicorn in a library! 🦄📚 Identifying these sneaky buggers requires a keen eye for detail and a dash of Sherlock’s logic.

Here are some clues to uncover logical flaws in your code:

• Check your assumptions: Are you sure that variable should be a string and not an integer? Double-check your assumptions to reveal hidden gremlins.
• Walk through your code step by step: Sometimes, a manual run-through of your code can reveal logic leaps that only a trained sloth could make! 🦥💨

Strategies to Debug and Fix Logical Errors

When it’s time to face the music and debug those logical gremlins, arm yourself with these battle-tested strategies:

• Rubber ducky debugging: Yes, you heard that right! Explain your code to a rubber ducky or an unsuspecting friend 🦆. The act of verbalizing can miraculously unearth logical bugs.
• Divide and conquer: Break down your code into smaller chunks and test each part independently. It’s like chopping up a coding puzzle into bite-sized pieces.

Stay tuned for the next part where we’ll tackle performance issues, module import errors, and dive into the fascinating world of exception handling in Python! 🎩✨

Anxiously awaiting Part 2? Stay tuned for more Python problem-solving shenanigans coming your way! 🚀

Thank you for bearing with me through this fun-filled Python programming adventure! Remember, coding is not just about solving problems; it’s about enjoying the journey. Happy coding, fellow Pythonistas! 🐍💻🌟

Program Code – Solving Common Python Programming Problems: Tips and Tricks

Code output:.

• Reversed string: olleh
• First non-repeating character: w
• Convert list to number: 1234

Code Explanation:

The Python code snippet above solves three common programming problems with efficient solutions.

• Reverse a string without using string functions: We define a function reverse_string that takes a string s as input and reverses it without using any built-in string functions. The method iterates through each character in the input string, prepending it to a new string, resulting in a reversed string.
• Find the first non-repeated character in a string: The function first_non_repeating_char finds the first character in a string that does not repeat. It uses a dictionary counts to keep track of character occurrences and a list char_order to remember the order in which characters appear. The function then iterates through the ordered list of characters and returns the first one with a count of 1. If all characters repeat or if the string is empty, it returns ‘None’.
• Convert a list of numbers to a single number: The function convert_list_to_number takes a list of numbers and converts them into a single number by concatenating their digits. This is achieved by first converting each number in the list to a string, using the map function, and then joining them together before converting back to an integer.

Each of these functions showcases a common problem-solving approach in Python, highlighting the language’s capability to implement concise and effective solutions with minimal code. The code is written with clarity and is meticulously commented to ensure understanding and readability, demonstrating how to address some typical Python programming problems in an efficient manner.

Frequently Asked Questions (F&Q) on Solving Common Python Programming Problems: Tips and Tricks

Q1: what are some common python programming problems that developers face.

A: Common Python programming problems include issues with syntax errors, logic errors, and handling exceptions. Understanding these common pitfalls can help developers write more robust code.

Q2: How can I efficiently debug Python programming problems?

A: Use debugging tools like print statements, logging, and Python’s built-in debugger (pdb). Also, consider using IDEs with debugging capabilities such as PyCharm or Visual Studio Code .

Q3: Are there any tips for optimizing Python code to prevent performance problems?

A: Yes, optimizing Python code involves techniques like using appropriate data structures , avoiding unnecessary loops, and utilizing libraries like NumPy for numerical computations.

Q4: What should I do when facing challenges with Python package dependencies?

A: Managing dependencies in Python can be tricky. Utilize virtual environments (e.g., virtualenv or conda), use a package manager like pip , and consider using requirements.txt files to manage dependencies effectively.

Q5: How can I stay updated on best practices for Python programming to avoid common pitfalls?

A: Stay connected with the Python community through forums, blogs (like RealPython and Python.org), attend Python meetups or conferences, and follow influential Python developers on social media for valuable insights.

Q6: Is there a recommended approach for handling file I/O errors in Python programming?

A: When dealing with file I/O errors, use try-except blocks to catch specific exceptions (e.g., FileNotFoundError) and handle them gracefully. It’s also essential to close files properly to prevent resource leaks.

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Two Python problems to solve

Goodnight. I have two problems and I would like help from my colleagues to resolve them. Are they:

Problem 1) Suppose someone presents you with a bag containing an odd number of numbers, be it 2m+1, with m > 300,000,000. Also, be told that the numbers appear in pairs, with the exception of one of them. For example, inside the bag, S, there could be S = [[4, 6, 8, 6, 4]]. Find out what this different number is. Explain how much time and memory your algorithm takes to solve this problem.

Problem 2) Suppose you are asked to find the ranking of an integer m in the sequence L from 0 to n, be it r(n) in which chains with at least two consecutive 1’s are prohibited. For example, r(000) = 1; r(001) = 2, r(010) = 3, r(011) = 3 and r(100) = 4. Note that r(011) contains a prohibition and therefore, r(010) = r(011) = 3 .

By which you mean: You have homework.

What have you done so far?

I’m starting to work on this problem now and I need help unlocking a resolution for these 2 problems. I’m not experienced with Python and I just started a few months ago.

The problems are not Python-specific. It’s more about creating the methods to resolve them.

Sounds like you need to counter the occurences of each number by scanning them all. Then pick the odd number based on its count. Try writing some code to do these 2 things.

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Common Problems Faced by Students Learning Python

The most common problems faced by students learning python.

Learning to code is an exhilarating journey filled with challenges, difficulties, and struggles. As programming students dive headfirst into the world of coding, they are faced with a myriad of obstacles that test their problem-solving skills and determination.

One of the most common problems faced by students learning Python coding is grasping the fundamental concepts of programming. The programming subject itself is vast and complex, with various languages, syntaxes, and algorithms to learn. This can be overwhelming for beginners who are just starting their coding journey. However, with dedication and perseverance, these students soon realize that every hurdle they overcome brings them one step closer to becoming proficient programmers.

Another difficulty that coding students encounter is the constant need for practice. Programming is not a subject that can be mastered by simply reading textbooks or attending lectures. It requires hands-on experience and practical application. This means spending countless hours coding, debugging, and troubleshooting programs. It can be frustrating when code doesn’t work as expected or when errors seem impossible to resolve. However, these struggles are what make programming such a rewarding field. Each obstacle overcome brings a sense of accomplishment and a deeper understanding of the intricacies of coding.

Additionally, programming students often face the challenge of staying up-to-date with the ever-evolving world of technology. Programming languages and frameworks are constantly being updated, introducing new features and functionalities. This means that students must continuously adapt and learn new skills to stay relevant in the industry. While this can be overwhelming at times, it also presents exciting opportunities for growth and innovation.

Common Problems faced by Python Students

1) lack of quality learning materials.

One of the primary challenges for students learning Python programming is the availability of quality learning materials. Many beginners struggle to find reliable and comprehensive resources that effectively teach the fundamentals of Python. The abundance of online tutorials, books, and videos can be overwhelming, making it difficult for students to determine which ones are trustworthy and provide accurate information.

To overcome this challenge, it is crucial for students to carefully select their learning materials. Opting for reputable online learning platforms, such as Practity , or referring to well-reviewed books can help ensure the quality and accuracy of the content. Additionally, seeking recommendations from experienced programmers or joining Python programming communities can provide valuable insights into the best learning resources available.

2) Ineffective Lectures and Labs Approach

Another common problem faced by students learning Python programming is the ineffective delivery of lectures and labs. Traditional classroom environments often rely on lectures that follow a one-size-fits-all approach, which may not cater to the diverse learning styles and paces of individual students. This can result in some students feeling left behind or disengaged from the material.

To address this issue, instructors should adopt a more interactive and engaging teaching approach. Incorporating Python exercises, group discussions, and real-world projects can enhance students’ understanding and retention of Python concepts. Additionally, providing access to virtual labs or coding platforms where students can practice coding in a sandbox environment can further reinforce their learning experience.

3) Lack of Problem-Solving Skills

Python programming requires strong problem-solving skills, as it involves breaking down complex problems into smaller, manageable tasks and devising efficient solutions. Many students struggle with developing this crucial skill, which can hinder their ability to write clean and optimized code.

To improve problem-solving abilities, students should practice solving coding challenges and puzzles regularly.  Additionally, collaborating with peers and participating in coding competitions or hackathons can provide valuable opportunities for students to apply their problem-solving skills in a competitive and supportive environment.

4) Time Management

Learning Python programming requires dedication and consistent effort. However, many students struggle with time management, especially if they are juggling multiple responsibilities such as work or other academic commitments. Poor time management can result in irregular study habits, leaving students feeling overwhelmed and unable to make progress.

To overcome this challenge, students should create a realistic study schedule and allocate dedicated time for learning Python or any other programming languaje. Breaking down the learning material into smaller, manageable chunks and setting specific goals for each study session can help students stay organized and motivated. Additionally, leveraging time management tools and techniques, such as the Pomodoro Technique, can enhance productivity and focus during study sessions.

5) Lack of Self-Confidence

Learning a new programming language can be intimidating, and many students struggle with self-confidence. They may doubt their abilities, compare themselves to more experienced programmers, or fear making mistakes. This lack of self-confidence can hinder their progress and prevent them from fully exploring and experimenting with Python programming.

To build self-confidence, students should celebrate their achievements, no matter how small, and embrace the learning process as a journey. It is important to remember that making mistakes is a natural part of learning and that every programmer, regardless of their level of expertise, has encountered challenges along the way. Seeking support from peers, mentors, or online communities can provide encouragement and reassurance during moments of self-doubt.

The Role of the Instructor

The role of the instructor is crucial in keeping students motivated and engaged during their Python programming learning journey. A skilled instructor should not only possess a deep understanding of Python concepts but also have the ability to communicate and explain complex ideas effectively. They should create a positive and inclusive learning environment that encourages students to ask questions, seek clarification, and actively participate in discussions.

Instructors can also incorporate gamification elements into their teaching approach, such as coding challenges, quizzes, or interactive coding exercises. These elements can make the learning experience more enjoyable and foster healthy competition among students. Additionally, providing timely and constructive feedback on students’ code can help them identify areas for improvement and boost their confidence.

Self-Taught Students

While self-taught students have the advantage of flexibility and independence, they often face additional challenges in their Python programming learning journey. Without a structured curriculum or guidance from an instructor, they may struggle with understanding complex concepts, finding appropriate learning resources, or identifying their own weaknesses.

To overcome these challenges, self-taught students should actively seek out online learning communities and forums where they can connect with experienced programmers and seek guidance. Participating in coding bootcamps or online courses specifically designed for self-paced learners can also provide a structured learning path and access to mentorship opportunities. Additionally, regularly reviewing and reflecting on their progress can help self-taught students identify areas for improvement and adjust their learning strategies accordingly.

Tips to Overcome Challenges in Learning Python

To conclude, here are some practical tips to help students overcome the challenges they may face while learning Python programming:

• Choose high-quality learning materials from reputable sources.
• Seek interactive and engaging learning experiences through hands-on exercises and group discussions.
• Practice problem-solving regularly through coding challenges and competitions.
• Manage your time effectively by creating a realistic study schedule and setting specific goals.
• Build self-confidence by celebrating achievements and seeking support from peers and mentors.
• Find an instructor or mentor who can provide guidance and motivation throughout your learning journey.
• Participate in online communities and forums to connect with fellow learners and experienced programmers.
• Consider joining coding bootcamps or online courses for a structured learning experience.
• Reflect on your progress regularly and adjust your learning strategies as needed.

Perseverance and a growth mindset are essential traits for success in programming. Embrace the process, seek support when needed, and never stop exploring the vast possibilities of Python programming.

“Learning to code is not about being perfect, but about being persistent and continuously improving.” – Unknown

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