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Difference Between Insertion sort vs Bubble sort

Computer Science comprises various data structures and algorithms that the user may implement for executing to complete a certain task or to solve any problem. Some classical algorithms are most familiar, such as bubble sort, insertion sort and merge sort, and others.

While talking about Insertion sort, it is an easy sorting algorithm that functions in the same way as we sort the playing cards in our hands. Here, the array will be practically split into a sorted as well as an unsorted portion. After that, the values available from the unsorted portion will be picked and then positioned correctly in the sorted portion.

Also, on the other side, the Bubble sort, which is also stated as comparison sort, is the easiest but quite ineffective type of algorithm for sorting, which goes through the list iterating, comparing neighboring items, and doing required swapping if present in an improper order. This bubble sorting is said to be necessary to know since it represents the basic foundations of sorting.

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Key differences between Insertion sort vs Bubble sort

Some of the key differences between Insertion sort vs Bubble sort are given below:

Basically, sorting can be defined as assembling elements either in descending or ascending order. This technique is approximately classified as internal and external sorting. In the internal sorting, all elements to be sorted are present in the main memory. Similarly, in the external sorting, when few sorts cannot be executed in the main memory, then they are performed on tape or disk.

In sorting algorithms, Bubble sort is to be considered as the humblest one. Here, the method visits through the entire array and then relates each neighboring number, where it interchanges the numbers, and it lasts till the list be present in ascending order.

Insertion sort algorithm places an unsorted item at its suitable position in every iteration that occurs through the array. This InsertionSort() function repeats over the array as well as evaluates the two elements at a time with the following algorithm instructions supposing that we need to sort an array having size as v in rising order:

Repeat from arr[1] to the last arr[v] over the array.

Match the current item or key to its predecessor.

If the predecessor is greater than the key element then, it will be compared to the items before.

Shift the larger items one place up so that it helps to create space for the swapped item

In addition, when we merge the concepts of sorting algorithm techniques used in both the InsertionSort() and BubbleSort() functions, then it will create a new sorting method known as Merge Sort or the MergeAndSort() function, which works with two arrays while splitting array to compare and sorting respectively. This process may take some time.

For sorting algorithms, numerical order, as well as lexicographical order, are mostly implemented. In addition, these sorting algorithms give an introduction to a variety of core algorithm ideas that include Big O notation, data structures, divide and conquer algorithms, best, average and worst-case analysis, lower bounds, and time-space tradeoffs.

For classic sorting algorithms, bad behavior is denoted by O(n2), and good behavior is denoted by O(n log n). Few of the algorithms may be recursive or non-recursive.

Bubble sort is also known to be Sinking Sort which iterates through the list of data and sorts the adjacent items using the swap technique to avoid wrong order.

Taking an illustration as a list:

After this, the second pass and a needed third pass will continue unless we get the sorted list. Thus, the Bubble sort goes through the whole array of elements in a pass comparing neighboring ones.

Taking the previous list for illustrating the insertion sort, then it will work through a pile, firstly getting an element and matching to the primary item, if found greater swaps and then again taking two elements and sorting starts for sorted position and ends up till all elements are invalid order as:

After this, as the list shows an ascended order, the algorithm or insertion sort stops further iteration and generates the output (12358).

Comparison table between Insertion sort vs Bubble sort

Please find the below comparison table for Insertion sort and Bubble Sort with few points:

Insertion Sort Bubble Sort

It is a simple type of sorting algorithm that creates the last sorted list by shifting one element at a time. It is a modest algorithm for sorting, which iterates through the list by matching contiguous pairs and then changing them when found in the wrong order.

Relocates an item at a time to the partly sorted array. Checks the adjacent items and then swaps them consequently.

This sort is twice as quick as bubble sort. This sort is leisurelier than the insertion sort.

A bit complex than bubble sort. It is quite an easy and simple one, not much difficult.

Best case complexity: O(N) Best case complexity: O(N)

It is adaptive as it delivers a minimum number of steps concentrated for the partially sorted array. Implements optimized approach, have fewer lines of code being easy to read and is able to be plugged in anywhere in the program.

It is stable with less number of swaps with an equally fast running case. It also defines a stable sort that will not alter the relative order of items having similar keys.

It is proficient for small data sets, and this Insertion sort works in the same way as we sort the playing cards. Bubble sort is actually very beneficial when a user needs to check the top x values available in a list.

Time complexity is O(n+d). Here, the d denotes the count of inversions. Time complexity is O(n^2).

In place, it means this sort needs just a constant amount O(1) of extra memory space. In place, it means using no auxiliary type data structure; the input is transformed having small memory space utilization.

Online: this sorting algorithm is able to sort a list of records as it receives them. It also operates on the data for sorting as delivered.

Conclusion

Insertion and Bubble sorts are helpful standard algorithms required for ordering data records properly. If the records can be sorted resourcefully, then that adds value to the sorting algorithms.

If the sorting algorithms work efficiently, then the time complexity of a given problem can be minimized. But anyhow, among the two sorts, the bubble sort is slower than insertion one as well as the simplest and easy one to execute.

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## How To Sort A List In Python

Sorting is an essential task in programming, and Python provides several ways to sort a list. A list is a mutable sequence data type that stores a collection of items, such as integers, strings, or objects. Sorting a list means arranging its elements in a specific order, such as ascending or descending. In this article, we will explore the different ways to sort a list in Python, including built-in functions, methods, and external libraries

Sorting a List using Built-in Functions

Python provides two built-in functions to sort a list: sorted() and sort(). Both functions can sort a list in ascending or descending order, depending on the optional reverse parameter.

The sorted() Function

The sorted() function returns a new sorted list, leaving the original list unchanged. The syntax of the sorted() function is as follows:

sorted(iterable, key=None, reverse=False)

iterable: The iterable to be sorted, such as a list, tuple, or string.

key: A function that takes an element of the iterable as input and returns a value to use for sorting.

reverse: A Boolean value that indicates whether to sort the iterable in descending order (True) or ascending order (False, default).

Here is an example that sorts a list of integers in ascending order using the sorted() function:

numbers = [3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5] sorted_numbers = sorted(numbers) print(sorted_numbers) # Output: [1, 1, 2, 3, 3, 4, 5, 5, 5, 6, 9]

You can also sort a list of strings in ascending order using the sorted() function:

fruits = ['apple', 'banana', 'cherry', 'date', 'elderberry'] sorted_fruits = sorted(fruits) print(sorted_fruits) # Output: ['apple', 'banana', 'cherry', 'date', 'elderberry']

Note that the sorted() function returns a new list, so you need to assign the result to a variable to use it.

The sort() Method

The sort() method sorts the list in place, meaning that it modifies the original list. The syntax of the sort() method is as follows:

list.sort(key=None, reverse=False)

key: A function that takes an element of the list as input and returns a value to use for sorting.

reverse: A Boolean value that indicates whether to sort the list in descending order (True) or ascending order (False, default).

Here is an example that sorts a list of integers in descending order using the sort() method:

numbers = [3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5] numbers.sort(reverse=True) print(numbers) # Output: [9, 6, 5, 5, 5, 4, 3, 3, 2, 1, 1]

You can also sort a list of strings in descending order using the sort() method:

fruits = ['apple', 'banana', 'cherry', 'date', 'elderberry'] fruits.sort(reverse=True) print(fruits) # Output: ['elderberry', 'date', 'cherry', 'banana', 'apple']

Note that the sort() method modifies the original list, so you don’t need to assign the result to a variable.

Sorting a List using External Libraries

Python provides several external libraries for sorting more complex data types or implementing custom sorting algorithms. In this section, we will explore two popular libraries: NumPy and Pandas.

Sorting a NumPy Array

To sort a NumPy array, you can use the numpy.sort() function, which returns a new sorted array, or the numpy.ndarray.sort() method to sort the array in place. The syntax of the numpy.sort() function is as follows:

numpy.sort(a, axis=-1, kind=None, order=None)

a: The array to be sorted.

axis: The axis along which to sort the array. By default, -1 sorts the array along the last axis.

kind: The sorting algorithm to use. By default, ‘quicksort’ is used.

order: The field(s) to use for sorting structured arrays.

Here is an example that sorts a one-dimensional NumPy array in ascending order using the numpy.sort() function:

import numpy as np numbers = np.array([3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5]) sorted_numbers = np.sort(numbers) print(sorted_numbers) # Output: [1 1 2 3 3 4 5 5 5 6 9]

You can also sort a two-dimensional NumPy array along a specific axis:

import numpy as np numbers = np.array([[3, 1], [4, 1], [5, 9], [2, 6], [5, 3]]) sorted_numbers = np.sort(numbers, axis=0) print(sorted_numbers) # Output: [[2 1], [3 1], [4 3], [5 6], [5 9]] Sorting a Pandas DataFrame

Pandas is a popular library for data manipulation and analysis in Python, and it provides powerful data structures and functions for handling tabular data. Pandas DataFrames are two-dimensional tables with labeled rows and columns.

To sort a Pandas DataFrame, you can use the sort_values() method of the DataFrame object. The sort_values() method sorts the DataFrame by one or more columns, depending on the input parameters. The syntax of the sort_values() method is as follows:

DataFrame.sort_values(by, axis=0, ascending=True, inplace=False, ignore_index=False, key=None)

by: The column(s) to use for sorting the DataFrame.

axis: The axis along which to sort the DataFrame. By default, 0 sorts the DataFrame along the rows.

ascending: A Boolean value that indicates whether to sort the DataFrame in ascending (True, default) or descending (False) order.

inplace: A Boolean value that indicates whether to sort the DataFrame in place (True) or return a new sorted DataFrame (False, default).

ignore_index: A Boolean value that indicates whether to reset the index of the sorted DataFrame (True) or keep the original index (False, default).

key: A function that takes a column of the DataFrame as input and returns a value to use for sorting.

Here is an example that sorts a Pandas DataFrame by a single column in descending order:

import pandas as pd data = {'name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'], 'age': [25, 20, 30, 35, 28], 'salary': [50000, 40000, 60000, 70000, 55000]} df = pd.DataFrame(data) sorted_df = df.sort_values(by='salary', ascending=False) print(sorted_df) # Output: # name age salary # 3 David 35 70000 # 2 Charlie 30 60000 # 4 Eve 28 55000 # 0 Alice 25 50000 # 1 Bob 20 40000

You can also sort a Pandas DataFrame by multiple columns:

import pandas as pd data = {'name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'], 'age': [25, 20, 30, 35, 28], 'salary': [50000, 40000, 60000, 70000, 55000]} df = pd.DataFrame(data) sorted_df = df.sort_values(by=['salary', 'age'], ascending=[False, True]) print(sorted_df) # Output: # name age salary # 3 David 35 70000 # 2 Charlie 30 60000 # 4 Eve 28 55000 # 0 Alice 25 50000 # 1 Bob 20 40000 Conclusion

Sorting a list is a fundamental task in programming, and Python provides several ways to accomplish it. In this article, we explored the built-in functions sorted() and sort(), as well as the external libraries NumPy and Pandas. We also covered the relevant parameters and options for each method, including sorting by multiple columns and custom sorting functions. By understanding these methods, you can sort lists and other data types in Python efficiently and effectively.

## How To Sort By Date In Microsoft Excel

Before You Start Sorting Dates

The cell that contains your date is in the Date or General format. You can check the cell format from the Home tab. If the cell is not in the date format, you can select the fly-out menu in the Number section and choose Date or General.

How to Sort Dates in Excel?

You can use the Sort & Filter tool to sort cells from ascending to descending order and vice-versa. However, you do not have the flexibility to sort dates by days, weeks, and months through this filter. You will have to use the library functions Excel offers to sort dates in this manner.

Use Sort and Filter Tool

The sort and filter tool is a quick way to sort dates from highest to lowest value. This tool sorts the dates according to the year. You will have to choose different options depending on whether you want to sort your dates according to ascending or descending order.

On your workbook, select the cell range with the dates.

Under Order, select the fly-out menu.

To sort dates in ascending order, choose Newest to Oldest.

Use Excel Functions

If you wish to sort dates out by days, weeks, and months, you will first have to extract these data using the library functions. Then, you can use the Sort & Filter tool to sort your data from ascending to descending order or vice-versa. The following methods use dates in the mm/dd/yyyy format.

Sort Dates by Days

You might find sorting dates by days useful while working with deadlines. This way, you can view your employees’ approaching deadlines. Before entering the formula, ensure you have correctly entered your dates.

On your worksheet, create a next column named “days”. On the first empty cell on the column, enter the formula in the following format:

=DAY(cell)

Sorting Dates by Weeks

If you are dealing with longer deadlines, you can sort the dates by week. Create a new column named “weeks” and enter the formula in the following format on the first cell of the columns:

=WEEKDAY(cell)

Sorting Dates by Months

You would want to sort your dates by month to keep track of birthdays, anniversaries, or annual events. Create a new column named “Months” on the worksheet with the dates you want to sort dates by month. On the first cell in the Months column, enter the formula in this format:

=MONTH(cell)

How to Create a Macro to Automatically Sort Date in Excel?

Macros are an amazing way to automate tasks in Excel. If you frequently need to sort dates out in Excel, you can record a Macro and run it the next time you want to get the job done. This will save you a lot of time compared to repeating these methods manually.

You can start recording a Macro from the Developer tab. After you hit record, you can carry on to continue the above-mentioned methods. Remember that all your actions are recorded, so only perform actions you wish to automate. Excel will record your keystrokes and save them as a Macro in the Visual Basics code.

## 6 Awesome Websites You Can Use To Burst Your Filter Bubble

As humans, we are psychologically biased toward news that backs up our own viewpoints, and the rise of a personalized-to-your-preferences Internet may be magnifying this problem. As awareness of the algorithms that bubble us in grows, though, a whole genre of websites, mobile apps, and browser extensions is popping up to help get you outside your comfort zone.

What is a filter bubble, and why should you burst it?

Coined in 2010 by Eli Parisier, the term “filter bubble” refers to the phenomenon of being isolated from information that differs from your preferred viewpoints. While humans have a tendency to do this on our own by choosing to read certain sources, social media, search engines, and other information-age services have built their business around learning what you like and showing it to you – including news. It’s like listening to one side of a telephone call: you can infer what the other side is saying, but you’re probably missing some nuance.

Here are some balanced news aggregators that show you the actual reality rather than what you want to see.

1. AllSides

AllSides is a news aggregator that draws well-written articles on the same topic from three different websites – one each from the left, center, and right. They have actually patented their system for rating media bias using blind surveys (people rate articles’ bias without knowing the source), third-party research, and community feedback. They recognize that their system will never be perfect, but you’re unlikely to find any other sources trying this hard to be balanced.

Unfortunately, they do not currently have an official app, though they do have a Chrome extension that lets you know the AllSides rating for the site you’re visiting.

2. The Perspective

3. Perspecs News

Perspecs is a publication from the Trinity Mirror, one of Britain’s largest newspaper publishers. While it lacks the transparency and scientific approach of AllSides and isn’t quite as sleek as The Perspective, it does have some nicely-made apps. It focuses not only on left, center, and right political news, but also on other controversial news with a yes/no side, which gives it some variety and makes it interesting to read. The fact that it’s owned by a U.K newspaper group may raise some questions about bias, but the material is usually interesting and intuitively presented.

4. CivikOwl

CivikOwl is an owl that lives in your Chrome browser and tells you about “hoo” you’re reading. It draws data from AllSides and MediaBiasFactCheck to analyze the credibility and left/right bias of the publication as well as using its own machine-learning algorithm to vet the quality of the article’s sources. If it can find alternative takes on the same story, it will show you those as well. It’s lightweight, unintrusive, and fun to use – who doesn’t want to hear what a civically-minded owl has to say about your news?

Read Across the Aisle is a Chrome extension that turns your browser into a comprehensive analysis of your news habits, welcoming you to each new tab with a visual representation of the percentage of your time that you spend on certain media sites. If it sees you spending too much time reading from sources on one side of the aisle, it will helpfully suggest that you try taking a look at another viewpoint.

The only downside: if you’re not a Chrome user you’ll need to start reading all your news in Chrome to get an accurate analysis. It also comes as an iOS app, but it only tracks stories that you read within the app itself.

6. PolitEcho

It’s a pun on “politico,” funny, right? It’s also an interesting way to analyze your Facebook friend group and see how balanced they are politically. You install PolitEcho in Chrome, hook it up to your Facebook, and let it analyze your friends’ Likes and posting frequency. When it’s finished, Politecho returns a graph showing you where your friends most likely fall on the left-center-right spectrum.

If Cambridge Analytica has made you justifiably nervous about third-party apps rifling through your data, don’t worry — Politecho does all the data-crunching locally, on your own computer, and doesn’t send your personal data anywhere. While it’s hardly perfect, it really works best for your hardline left/right friends.

Are these sites and apps the answer?

The Internet provides easy access to an almost infinite amount of information, and these sites and apps can help you find some things you might otherwise miss. However, bubbles and echo chambers were problems long before the Internet and probably aren’t going away anytime soon. These tools aren’t a cure by any means, but they’re a great place to experiment with getting outside your informational comfort zone.

Andrew Braun

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## Power Bi Vs Tableau Vs Qlik

Difference Between Power BI vs Tableau vs Qlik

Power Bi is a Business Intelligence tool we can upload data and publish data throughout our companies. Business Intelligence response to any query and improves decision making. Adding power to the business for good visualization of data. Another feature of Power BI Is Quick Insights in which we can search a dataset for interesting patterns and provides a list of charts for a better understanding of data. It uses artificial intelligence and data mining to analyze the data. Qlik is also a Business Intelligence and data visualization tool.

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It is an end – to end ETL solution yielding good customer service. With qlik, we can create a flexible end-user interface, make good presentations based on the data, creates dynamic graphical charts and tables, perform statistical analysis, builds own expert systems. Qlik can be used with the virtual database. It is a window-based tool that requires the following components: Qlik Server, Qlik Publisher.

Features of Power BI

Rich Graphical visualizations from complex BI data.

Includes a dataset with customizable dashboards.

Features of Qlik

With the use of a hybrid approach, the user can associate data stored within big sources are stored within the Qlik view in-memory dataset.

With the help of direct discovery, they allow users to perform Business discovery and visual analysis.

Interact with dynamic apps, Dashboards.

Features of Tableau

They have a good drag and drop.

For data sharing, they use Tableau Public.

They implement interactive data visualizations on the web.

Their performance is strong and reliable and operates on huge data.

They are mobile-friendly and supports full online versions.

Below is the top 9 difference between Power BI vs Tableau vs Qlik

Key Differences Between Power BI and Tableau and Qlik

These are popular choices in the market; let us discuss some of the major difference:

Qlik can be immediately accessed by multiple users. Qlik is faster than Tableau. Power bI connect to any data source they do not require ETL.

The documents are stored in. the qvw format we can access these documents via Qlik Views proprietary communication protocol and stored in Windows OS, and all the events are taken in Qlik Server, they are responsible for Client-Server Power BI has three types of files excel(.xls), power BI desktop(.pbix), (.csv). Tableau extract files can have (.tde) file extensions.

Qlik structure is not well managed whereas Tableau structure is managed by well user guide.

Qlik works as a standalone technique. Publishing the data to the outside world are managed by QlikView Publisher. Power BI is available only on the SAAS model whereas Tableau has cloud and on-premises option. Power BI desktop version is free.

Data modeling facilities are increased using POwerBI. In Qlik Data insights are generated rapidly.

Tableau and Power BI is user-friendly. Qlik has high customizable patterns.

Qlik and tableau work for statistical analysis. Power BI does not have this capability.

Power BI vs Tableau vs Qlik Comparison Table

Below is the 9 topmost comparison

The basis of comparison  Power BI Tableau Qlik

Performance  It Lacks behind on data visualizations. They are more User-friendly because Non- technical users can work with this tool. They use cubic Technique Qlik needs a developer to work with reports and dashboards. They take all types of datasets.

They have good visualizations.

User Interface

Dashboards are the key feature of PowerBI when has a good User interface to publish the report.

User Interface is better

User Interface is quite down when compared with Tableau.

Ease of learning

User-friendly- Knowledge of Excel is enough

They do not require any technical or programming skills to work with.

Easy to learn with Data science background.

Supportive requirements

Power BI has Power BI desktop, Gateway

They work with front-end tool such as R.

Qlik consists of both front-end (Qlik Developer) and back-end (Qlik Publisher)

Version

The desktop version is free, Power BI Pro is pay per month. Tableau Reader is a free version. Tableau Server is a licensed one. Qlik Personal Edition is a free version of Qlik and runs without a license Key.

Cost- Effectiveness Less expensive. Qlik website has two editions. Personal version is free, Enterprise version can be used contact with the team.

Online Analytical Programming They connect to OLAP cubes via SQL servers for multidimensional analysis. Tableau can connect to OLAP taking out the cube measures at the deepest level. Access to OLAP provides encapsulated data views.

Speed They have smart recovery Speed depends on RAM and data sets. They have better Speed since they store the data in the server RAM (In-Memory Storage)

Advantage Power BI are inexpensive and have scalability for larger projects. They are top ranked in intelligence visualizations. They provide wide range deep analytics and they have good customer satisfaction

ratings.

Conclusion Recommended Articles

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## Power Supply Ratings Explained: 80 Plus Platinum Vs. Gold Vs. Bronze Vs. White

Efficiency ratings explained

A power supply’s efficiency rating tells you how much energy makes the conversion from AC power (what it draws from the wall) to DC power (what your PC’s components run off of). The rest is lost as heat. Power supplies rated as 80 Plus will have a 80-percent power to 20-percent heat ratio at 20-percent, 50-percent, and 100-percent load. As you get into the precious metal designations, the efficiency rises, capping out at 94-percent under 50-percent load for a Titanium-rated model. The efficiencies for power supplies rated at 80 Plus Bronze and above vary based on the amount of load.

The more efficient a power supply is, the less power it uses and the less heat it generates—which means a lower electricity bill and a quieter PSU. The components tend to be better too, which means you should be able to use it for longer. Manufacturer warranties usually reflect this: A Bronze-rated power supply might get a 3- to 5-year warranty, while a Platinum-rated model will be backed for 10 years.

These days, the most power supplies from well-known, reliable manufacturers have an 80 Plus or above certification, but it is possible to find other PSUs that fall below that. Those cheap power supplies that get included as part of a bundle with ultra-cheap cases? Not 80 Plus-rated.

The actual percentage of efficiency for the U.S. and other countries with electrical grids that run on similar voltage. (You can see the full chart on CLEAResult’s site.) The minimum efficiency to meet ATX specifications is 60-percent at 50-percent load—much lower than where the 80 Plus standard begins.

How to choose an efficiency rating for a power supply

Three main considerations help determine what efficiency rating will work best for your PC build:

Local electricity rates

Ambient temperatures

Budget

Most average PC users who live in a temperate climate with low pricing for electricity will do fine with a standard 80 Plus or 80 Plus Bronze power supply. As you can see in the table above, the efficiencies don’t dramatically jump as you move to a higher rating.

EVGA

Typically as you go up in wattage, power supply efficiency ratings rise as well. But efficiency ratings don’t always tell the whole story about a specific model’s performance, so be sure to read reviews to get the full picture.

If you live someplace where electricity is more expensive, then the cost savings of a more efficient power supply can be worth the higher initial price. A higher-rated PSU can also make sense if you live somewhere that gets very hot for long periods, as ultra-warm ambient temperatures can reduce a power supply’s efficiency. Less heat from your power supply means less noise from its fan and less work on your part to keep your PC cool overall, too.

In general, the higher you climb in wattage, so too does the efficiency rating. Your decisions will typically fall between just two to three options, which will make the selection process easier. Don’t feel obligated to spend more than you need—budget still matters. Buy a power supply with an efficiency that fits your situation, not the demands of other people who believe you must always maximize on specs.

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