Trending March 2024 # Top Rarely Used Pandas Function In 2023 One Should Know # Suggested April 2024 # Top 6 Popular

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Introduction

When it comes to data preparation using Python, the term which comes to our mind is Pandas. No, not the one whom you see happily munching away on bamboo and lazily somersaulting.

Well, a library for prepping up the data for further analysis. Be it finding out which columns need to be removed or which features are to be created from the existing features. Pandas help us with all these questions. Ev anything which requires your data to be prepped up for future analyzing steps, Pandas comes up as a handy .

All of the functions we’ll discuss now fall under the data preprocessing step. The preprocessing step is an essential step, be it from the view of data preprocessing or the building of a machine learning model over the data. It helps clean, transform and prepare the data for further analysis. In many cases, the raw(un-processed) data might be incomplete and have some errors, which is well addressed when data preprocessing is done.

Here, in this article, we’ll quickly see some of sic Pandas functions, which are not much used but can be very useful and convenient, especially if you’re a newbie or noob 😛

Let’s dive in!

Learning Objectives

Understanding some rarely used python data analysis functions.

Illustrating how to use these pandas functions effectively with examples of real world-scenarios and understanding it better with code snippets.

To show the benefits of these pandas functions and how they help in data preprocessing and analysis.

Hands-on examples and code snippets for the readers to practice and implement.

Table of Contents

Ffill and Bfill Function

Shift Function

Select_Dtypes Function

Clip Function

Query Function

Melt Function

Where Function

Iat Function

Conclusion

There are many instances where data in certain places don’t get captured, resulting in missing values. We need to treat the missing values so that it doesn’t lead to incorrect results in the long run when building a machine learning model on the same data. So, we would substitute the missing values which is called Missing Value Imputation. There are many ways to impute the missing values, but we’ll be discussing the functions ffill(forward fill) and bfill(backward fill). Let’s quickly see them. But what’s the basis on which we are imputing the values? In general, it’s a good idea to use forward fill (ffill) or backward fill (bfill) when the data has a temporal ordering and you want to propagate the last known value forward or backward in time, respectively, to fill in missing values.

We have the following dataframe. And we want to fill in the missing values.

Here, we’ll be taking an example of a time series dataset and imputing the missing values with the ffill function. If we observe carefully, we can see an increasing trend in the dataset. Let’s have a quick look at the dataset.

We’ll be imputing the following missing values using the forward fill function.

After applying the following code, the values will get imputed in such a way that the previous value to the missing value would get forwarded.

df1 = df1.ffill(axis=0) df1

After applying the code, we get the following output. If we observe carefully, then we can see that only one observation has not been replaced. Why? Because we don’t have a value preceding the first observation.

Notice how the value in the 4th row, 2nd column has a value that it has copied from its previous cell. Forward cells are filled with the value of its previous observation.

Although there are many ways to impute this particular value(0th row, 1st column), here we’ll be using the bfill method to impute this value.

Let’s take the same dataset and perform the bfill function.

df1 = df1.bfill(axis=0) df1

Observe the above output, where the missing values are imputed with the value following it. The value in the 1st observation(0th row) and 2nd column has been imputed with the value of the 2nd observation(1st row) and 2nd column and the same has happened in the following rows.

The ffill (forward fill) and bfill (backward fill) functions are commonly used in real-life scenarios for data pre-processing and handling missing values. Some use cases are:

Financial Data Analysis: Financial data often contain missing values due to various reasons, such as stock market holidays or delayed data releases. bfill can be used to backward-fill missing values with the next available value.

Climate Data Analysis: Climate data often contains missing values, such as when a weather station is not functioning or data is lost. ffill and bfill can be used to fill missing values with the nearest available data, allowing for continuous analysis.

Energy Consumption Data Analysis: Energy consumption data can contain missing values due to meter reading errors or equipment failure. Both ffill and bfill can be used to fill in missing values in energy consumption data, allowing for accurate analysis of energy consumption patterns.

Shift Function

This pandas function shifts the element to a desired location as per the desired number of periods we enter as a parameter. This function can work on both columns and also on rows.

Let’s see an example, where we can see the working of the shift function. Let’s say we have a weather dataset, and we wish to create new columns which contain the weather measurements from the previous day. We have the following weather data with us.

And now, we wish to create a new feature, such as the previous day’s temperature. These kinds of features would, in turn, be helpful as input features for the machine learning model to predict future weather.

So in order to fill the new features with values, we would shift the original features such as the temperature column by one 1 day.

df[‘prev_day_temp’] = df[‘temperature’].shift(1) # we’ll remove the first row as it has a null value df = df.dropna() df

Some real-life scenarios where we use the shift function are as follows.

Seasonality Analysis: The shift function can be used in seasonality analysis to shift data by one seasonal cycle. This allows for the comparison of data across different seasons and can be useful for identifying seasonal trends and patterns.

Time-series Data Analysis: In time-series data, the shift function can be used to shift data by a specified number of periods, allowing for data to be compared across different time frames. This can be useful for analyzing trends and patterns in data.

Data Alignment: The shift function can be used to align data from different sources, such as data collected at different times or locations. This can be useful for ensuring that data is comparable and suitable for analysis.

Select_Dtypes Function

This function includes or excludes columns of specific datatypes. Let’s take the example of the students’ health dataset, which contains information(name, age, class roll no) about the college students and their bmi.

Now, let’s include only integer datatypes from our dataset.

We run the following code:

df.select_dtypes(include='int')

Let’s exclude the object(string) datatype from our dataset.

df.select_dtypes(exclude='object')

A few examples where we can get to see the use of the select_dtypes function are as follows:

A dataframe containing information about employees in a company, with columns like “employee_name”, “hire_date”, “salary”, and “department”. You want to analyze the salary data, so you use select_dtypes to filter the dataframe and select only the “salary” column, which is of float data type.

A dataframe containing customer reviews for different products, with columns like “reviewer_name”, “review_date”, “star_rating”, and “review_text”. You want to analyze the star rating data, so you use select_dtypes to select only the “star_rating” column, which is of int data type.

A large dataframe containing information about various customers and their purchases. The dataframe has columns like “customer_name”, “purchase_date”, “purchase_amount”, and “product_category”. You want to analyze only the numerical columns, like “purchase_amount”, to understand spending patterns.

Clip Function

We’ll be running the following code.

sub_data.clip(20.0, 50.0)

If we look at the output, we can see that all the values in the Weight column are greater than 50.0, which is the upper bound. Hence, all the values are replaced by the upper bound limit.

A few examples of the clip function, where we can see its real-life usage.

A dataframe containing employee salaries, with a column “salary”. The company has a policy of capping the maximum salary at a certain value. You can use the clip function to set all salaries above the cap to the cap value.

A dataframe containing energy consumption data for different households, with a column “energy_consumption”. You want to clip the values below a certain minimum to 0, to represent that the minimum amount of energy used in a household is 0.

A dataframe containing stock prices for different companies, with a column “stock_price”. You want to clip the stock prices so that any value above a certain threshold is set to the threshold value. You can use the clip function to achieve this.

Query Function

Let’s take an example of a dataset and understand how this works. So, we have a dataset where we have details about customers visiting a hotel, their total_bill, tip_given, and time of ordering(lunch or dinner). I have used the chúng tôi file over here. You can download the same from this link.

import pandas as pd df = pd.read_csv('tips.csv') df.head(10)

Let’s say I only want to view those observations which have details of female customers who have ordered Lunch or Dinner and have sizes more than or equal to 2.

We can use the query function to get those observations.

food_time = ['Lunch','Dinner'] # the query result should include both dinner and lunch sub_df.tail(10)

Here are some real-life examples to better understand the usage of the query function.

A dataframe containing information about different countries, with columns like “country_name”, “population”, “area”, and “continent”. You want to analyze the data for countries with a population greater than 100 million. Based on this condition, you can use the query function to filter the dataframe.

A dataframe containing information about different sports teams, with columns like “team_name”, “sport”, “wins”, and “losses”. You want to analyze the data for teams with a win-loss ratio greater than 0.5. You can use the query function to filter the dataframe based on this condition.

A dataframe containing information about different movies, with columns like “movie_title”, “release_date”, “rating”, and “genres”. You want to analyze the data for movies with a rating greater than 8.0. You can use the query function to filter the dataframe based on this condition.

Melt Function

So, we will use the melt function.

df_mark=df_mark.melt(id_vars='Name', var_name='Sem_Month', value_name = 'Percentage') # id_vars - the column's name to be used as identifier variables. # var_name - the name to be used for the 'variable' column # value_name - the name to be used for the values under the previous columns df_mark

The above output seems a bit different than what we had expected. Just observe the Sem_Month column. Let’s just sort the values based on the Name and Sem_Month columns.

df_mark = df_mark.sort_values(['Name','Sem_Month']) df_mark

We’ll just reset the index of our dataset to make it look better.

df_mark.reset_index(drop=True)

Real-life examples where we use the melt function.

A dataframe containing exam scores for different students in different subjects, with columns like “Math”, “Science”, “English”, and so on. You want to analyze the exam scores, but the current format makes it difficult to do so. You can use the melt function to reshape the dataframe into a “long” format, with columns for “subject” and “score”, making it easier to perform analysis.

A dataframe containing the number of apples and bananas sold at a fruit stand, with columns like “Apples Sold” and “Bananas Sold”. You want to analyze the sales data, but the current format makes it difficult to do so. You can use the melt function to reshape the dataframe into a “long” format, with columns for “fruit” and “sold”, making it easier to perform analysis.

A dataframe containing the number of books read by different people, with columns like “Person A”, “Person B”, “Person C”, and so on. You want to analyze the book reading data, but the current format makes it difficult to do so. You can use the melt function to reshape the dataframe into a “long” format, with columns for “person” and “books_read”, making it easier to perform analysis.

Where Function

This function comes in handy when we wish to replace certain values in our dataframe. This checks one or more conditions and returns the result accordingly. In the following example, we have certain values as ‘Select,’ and we wish to replace them with the NaN values.

df_sub=df.where(cond=(df!='Select'), other='NaN') #keep the dataframe values as it is, if not equal to ‘Select’, otherwise replace it with ‘NaN’ df_sub

So what exactly is happening in the above code? We use the cond parameter to filter the dataframe or series based on a given boolean condition. Here, the condition is that replace the ‘Select’ string values with ‘NaN’ values, and if there’s anything other than the ‘Select’ value, then keep it as it is. For example, in the 2nd row, BMI column, the condition is true (20 is not equal to ‘Select’), this value won’t be replaced.

Note that, by default, even if we don’t specify the value to be replaced with, then it would replace it by NaN itself. Otherwise, if we want to replace the existing value with some value as per our needs, then that needs to be mentioned in the other parameter.

Once the above code is run, we get the following output. Notice how the values which were ‘Select’ earlier have been replaced with ‘NaN’ values.

A few real-life scenarios where the Pandas function can be used are as follows:

A DataFrame with customer data, and you want to replace all customers ages less than 18 with the value “minor”.

A dataframe having employee data. You want to highlight all employees who have been with the company for less than two years in red.

Analyzing sales data and want to identify all sales that are below the average sale amount.

Iat Function

Last but not least, let’s just quickly discuss the iat function. So let’s say that in the above Pandas function we replaced all occurrences of Select with NaN. But now, let’s say we wish to replace a particular occurrence of Select with some different value. So, here, comes the need for the iat

We’ll run the following code.

df.iat[3, 1] = ‘NaN’ # replacing the 3rd row, 1st column value by NaN df

The iat function is specific to the Pandas library in Python and is used to access a scalar value in a DataFrame or Series.

Conclusion

In this article, we learned about some of the Pandas functions used while working with Pandas to make our work easier and more efficient. These functions are some which save a lot of our time. These functions might not be as widely used as compared to other functions, but they should also be known to anyone, be it even a beginner.

Recap of key takeaways: So in this article, we learned about the importance of preprocessing. How is it important to prepare the data for further analysis? There might be various other functions that might help preprocess the data, but the functions discussed are some that are not known much but can be very useful.

Usefulness: The data on which we work is obtained from real-world scenarios, hence would contain missing values and would be dirty in most cases. Working with unprocessed or dirty data might bring inaccurate results, which can be dangerous in the long run when decisions are made based on that. Hence, preprocessing is important.

We understood the functions with code snippets, which would better our understanding and be very useful to someone starting off in their data analysis journey.

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Top Blockchain Interview Questions One Should Know About

These blockchain questions are coming at your interview!!

Blockchain is one of the hottest topics in the field of digital technology. Every aspirant of this field will face interview questions on blockchain one way or another. Here is the list of questions on blockchain that one should be aware of.  

How are transactions and blocks encrypted in the Bitcoin implementation?

Bitcoin blocks are not encrypted in any way: Every block is public. What prevents modifications and guarantees data integrity is a value called the block hash. Block content is processed using a special hash function—in the case of Bitcoin, it’s SHA256—and the resulting value is included in the blockchain.  

Explain why a blockchain needs tokens to operate.

Coins/tokens are used to implement changes between states. When somebody does a transaction, this is a change of state, and coins are moved from one address to another. Apart from that, transactions can contain additional data, and a change of state is used to mutate data—the only way to do this is in an immutable-by-definition blockchain. Technically, a blockchain doesn’t need coins for its essential operations, but without them, some other way needs to be introduced to manage states of the chain and to verify transactions.  

How does peer discovery work in a peer-to-peer (P2P) network?

When a new node boots up, it doesn’t know anything about the network, because there is no central server. Usually, developers provide a list of trusted nodes written directly into the code that can be used for initial peer discovery.  

How do verifiers check if a block is valid?

Every full node on the network does block verification. When a new block is announced, every node that receives it does a list of checks. The two most important checks are proof of work (if a block provides enough work to be included in the chain) and of the validity of all transactions (each transaction must be valid).  

What is a scriptPubKey? Explain how a P2SH address can be spent.

A scriptPubKey is a so-called “locking script.” It’s found in transaction output and is the encumbrance that must be fulfilled to spend the output. P2SH is a special type of address where the complex locking script is replaced with its hash. When a transaction attempting to spend the output is presented later, it must contain the script that matches the hash, in addition to the unlocking script.  

What is a trapdoor function, and why is it needed in blockchain development?

A trapdoor function is a function that is easy to compute in one direction but difficult to compute in the opposite direction unless you have special information. Trapdoor functions are essential for public-key encryption—that’s why they are commonly used in blockchain development to represent the ideas of addresses and private keys.  

What is mining?

Mining is the process of reaching consensus in blockchain networks. Mining serves two purposes. First, it creates new coins in a generated block. Second, it includes transactions in a distributed ledger by providing proof of work to the network; that is, proof that the generated block is valid.  

What is a chain fork?

Blocks in the ledger are included in such a way as to build the longest chain, i.e., the chain with the greatest cumulative difficulty. Forking is a situation where there are two candidate blocks competing to form the longest blockchain and two miners discover a solution to the proof-of-work problem within a short period of time from each other. The network is then divided because some nodes get blocks from miner #1 and some from miner #2. A fork usually gets resolved in one block, because the probability that this situation happens again gets extremely lower with the next blocks that arise, so soon there is a new longest chain that will be considered as main.  

Is the blockchain totally different from traditional banking ledger?

Banking ledgers are used to ensure that the transactions can take place correctly. That’s why they trace and timestamp transactions. The significant difference between a banking ledger and a blockchain is how they are governed. The blockchain is decentralized in nature; however, banking ledgers are completely centralized as banks govern them.  

What Is federated blockchain? Give examples

Blockchain is one of the hottest topics in the field of digital technology. Every aspirant of this field will face interview questions on blockchain one way or another. Here is the list of questions on blockchain that one should be aware of.Bitcoin blocks are not encrypted in any way: Every block is public. What prevents modifications and guarantees data integrity is a value called the block hash. Block content is processed using a special hash function—in the case of Bitcoin, it’s SHA256—and the resulting value is included in the blockchain.Coins/tokens are used to implement changes between states. When somebody does a transaction, this is a change of state, and coins are moved from one address to another. Apart from that, transactions can contain additional data, and a change of state is used to mutate data—the only way to do this is in an immutable-by-definition blockchain. Technically, a blockchain doesn’t need coins for its essential operations, but without them, some other way needs to be introduced to manage states of the chain and to verify chúng tôi a new node boots up, it doesn’t know anything about the network, because there is no central server. Usually, developers provide a list of trusted nodes written directly into the code that can be used for initial peer discovery.Every full node on the network does block verification. When a new block is announced, every node that receives it does a list of checks. The two most important checks are proof of work (if a block provides enough work to be included in the chain) and of the validity of all transactions (each transaction must be valid).A scriptPubKey is a so-called “locking script.” It’s found in transaction output and is the encumbrance that must be fulfilled to spend the output. P2SH is a special type of address where the complex locking script is replaced with its hash. When a transaction attempting to spend the output is presented later, it must contain the script that matches the hash, in addition to the unlocking script.A trapdoor function is a function that is easy to compute in one direction but difficult to compute in the opposite direction unless you have special information. Trapdoor functions are essential for public-key encryption—that’s why they are commonly used in blockchain development to represent the ideas of addresses and private keys.Mining is the process of reaching consensus in blockchain networks. Mining serves two purposes. First, it creates new coins in a generated block. Second, it includes transactions in a distributed ledger by providing proof of work to the network; that is, proof that the generated block is valid.Blocks in the ledger are included in such a way as to build the longest chain, i.e., the chain with the greatest cumulative difficulty. Forking is a situation where there are two candidate blocks competing to form the longest blockchain and two miners discover a solution to the proof-of-work problem within a short period of time from each other. The network is then divided because some nodes get blocks from miner #1 and some from miner #2. A fork usually gets resolved in one block, because the probability that this situation happens again gets extremely lower with the next blocks that arise, so soon there is a new longest chain that will be considered as main.Banking ledgers are used to ensure that the transactions can take place correctly. That’s why they trace and timestamp transactions. The significant difference between a banking ledger and a blockchain is how they are governed. The blockchain is decentralized in nature; however, banking ledgers are completely centralized as banks govern them.A Federated blockchain is a blockchain that is run by a group. This makes them faster and more scalable as the group dedicates the validation of the transactions. To get started, pre-selected nodes are made by leaders. These nodes dictate both the transactions and also the persons that can participate in the blockchain. Examples include EWF, R3, etc.

Working Of Cut() Function Pandas With Examples

Introduction to Pandas cut()

Web development, programming languages, Software testing & others

For example, let us say we have numbers from 1 to 10. Here, we categorize these values and differentiate them as 2 groups. These groups are termed as bins. Hence, we differentiate these set of values are bin 1 = 1 to 5 and bin 2 = 5 to 10. Now, once we have these two bins, we decide which values are greater and which are smaller. So, the numbers from 1 to 5 are smaller than the numbers from 5 to 10. Hence, these smaller numbers are termed as ‘Lows’ and the greater numbers are termed as ‘Highs’.

This method is called labelling the values using Pandas cut() function. Use cut once you have to be compelled to section and type information values into bins. This operate is additionally helpful for going from an eternal variable to a categorical variable. As an example, cut may convert ages to teams getting on supports binning into associate degree equal variety of bins, or a pre-specified array of bins.

Syntax of Pandas cut()

Given below is the syntax of Pandas cut():

Pandas.cut(x, duplicates='raise', include_lowest = false, precision = 3, retbins = false, labels = none, right = true, bins)

Parameters of above syntax:

‘x’ represents any one dimensional array which has to be put into bin.

duplicates represents the edges in the bin which are not unique values and thus returns a value error if not assigned as raise or drop.

include_lowest represents the values which have to be included as lowest values.

precision parameter is always represented as an integer values as it is the exact value which has to be displayed and stored by the bin numbers.

retbins are always represented as Boolean values and these are the parameters which help the user to choose which are the useful bins.

labels just helps to represent and categorize the bins as highs or lows. They can be Boolean or arrays.

right parameter checks if the bin is present in the rightmost edge or not and they are represented as Boolean values and assigned to either true or false.

bins just help to categorize the data and if it is an integer then the range for all values is defined as ‘a’ and this ‘a’ describes the minimum and maximum values. If the bin values are a series of scalar arrays then, the bins are not formed in a sequential format and finally the interval index defines whether the bins are overlapping or falling on one another or they are produced in a proper format in the output.

How cut() Function works in Pandas?

Given below shows how cut() function works in Pandas:

Example #1

Utilizing Pandas Cut() function to segment the numbers into bins.

Code:

import numpy as np import pandas as pd df_num1 = pd.DataFrame({'num': np.random.randint(1, 30, 20)}) print(df_num1) df_num1['num_bins'] = pd.cut(x=df_num1['num'], bins=[1, 5, 10, 15, 30]) print(df_num1) print(df_num1['num_bins'].unique())

In the above program, we see how to categorize the values into different bins. First we import numpy and pandas and then define the different integer values and finally add pandas.cut() function to categorize these values as bins and finally print them as a separate column and also print the unique values in the bins and thus the output is generated.

Example #2

Utilizing Pandas cut() function to label the bins.

import numpy as np import pandas as pd df_num1 = pd.DataFrame({'number': np.random.randint(1, 50, 30)}) print(df_num1) df_num1['numbers_labels'] = pd.cut(x=df_num1['number'], bins=[1, 25, 50], labels=['Lows', 'Highs'], right=False) print(df_num1) print(df_num1['numbers_labels'].unique())

Output:

Here, we do the same as previous but along with categorizing into bins, we also categorize these bins ad label them as highs and lows. We first import pandas and numpy packages in python. We later assign the values for the bins and by making use of pandas.cut() function, we differentiate the numerical values into bins and finally see which numbers are greater and which are smaller. So, the greater numbers are termed as highs and the smaller numbers are termed as lows.

Conclusion

Use cut after you rephased the sorted values into bins. This operation is additionally helpful for going from endless variable to a categorical variable. For instance, cut might convert ages to teams old-time ranges. Supports binning into Associate in Nursing equal variety of bins, or a pre-specified array of bins. The specific bins are solely go back only when the parameter retbins = true. Hence, for sequence bins which consist of scalar arrays, this will end up as the last array of the present bin. So, when duplicates=drop, the bins drop out the array which are non-unique and hence ends up offering adequate number of bins.

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We hope that this EDUCBA information on “Pandas cut()” was beneficial to you. You can view EDUCBA’s recommended articles for more information.

Lastpass Vs. Keepass: Which One Should You Use In 2023?

There’s a better way. Password managers will keep track of them for you, and LastPass and KeePass are two popular, but very different choices. How do they compare? This comparison review has you covered.

LastPass is a popular password manager that’s easy to use and offers a workable free plan. Paid subscriptions add features, priority tech support, and extra storage. It’s primarily a web-based service, and apps are offered for Mac, iOS, and Android. Read our detailed LastPass review to learn more.

LastPass vs. KeePass: Head-to-Head Comparison

1. Supported Platforms

You need a password manager that works on every platform you use. LastPass fits the bill, and works with all major operating systems and web browsers:

Desktop: Windows, Mac, Linux, Chrome OS,

Mobile: iOS, Android, Windows Phone, watchOS,

Browsers: Chrome, Firefox, Internet Explorer, Safari, Edge, Maxthon, Opera.

KeePass is different. The official version is a Windows app, and because it’s open-source, various individuals have been able to port it to other operating systems. Not all of these ports are of the same quality, and there are multiple options for each operating system, including:

5 for Mac,

1 for Chromebook,

9 for iOS,

3 for Android,

3 for Windows Phone,

3 for Blackberry,

1 for Pocket PC,

and more!

Those options can be confusing! There’s no easy way to know which version is best for you other than trying a few. When evaluating the app on my iMac, I used KeePassXC.

If you use KeePass on multiple devices, your passwords won’t be synced between them automatically. They’re stored in a single file, and you’ll have to sync that file using Dropbox or a similar service.

Winner: LastPass supports most popular platforms out of the box, while KeePass relies on ports by third parties.

2. Filling In Passwords

LastPass allows you to add passwords in a number of ways: by adding them manually, by watching you log in and learning your passwords one-by-one, or by importing them from a web browser or other password manager.

KeePass won’t learn your passwords as you type them, but it does allow you to add them manually or import them from a CSV (“comma-separated values”) file, a format most password managers can export to.

Some reviewers mentioned that the app can directly import from a number of other password managers, but the version I’m using doesn’t. KeePass can’t learn your passwords by watching you log in to websites.

Once you have some passwords in the vault, LastPass will automatically fill in your username and password when you reach a login page.

Once I found the right Chrome extension (in my case it’s KeePassXC-Browser), KeePass offered the same convenience. Prior to that, I found initiating a login directly from the app trickier and less convenient than other password managers.

Winner: LastPass. It lets you customize each login individually, allowing you to require that your master password be typed before logging into a site.

3. Generating New Passwords

Your passwords should be strong—fairly long and not a dictionary word—so they are hard to break. And they should be unique so that if your password for one site is compromised, your other sites won’t be vulnerable. Both apps make this easy.

LastPass can generate strong, unique passwords whenever you create a new login. You can customize the length of each password, and the type of characters that are included, and you can specify that the password is easy to say or easy to read, to make the password easier to remember or type when necessary.

KeePass will also generate passwords automatically and offers similar customization options. But you need to do this from the app rather than your browser.

Winner: Tie. Both services will generate a strong, unique, configurable password whenever you need one.

4. Security

Storing your passwords in the cloud may concern you. Isn’t it like putting all your eggs in one basket? If your account was hacked they’d get access to all your other accounts. LastPass takes steps to ensure that if someone does discover your username and password, they still won’t be able to log into your account.

You log in with a master password, and you should choose a strong one. For additional security, the app uses two-factor authentication (2FA). When you try to log in on an unfamiliar device, you’ll receive a unique code by email so you can confirm that it’s really you logging in.

Premium subscribers get additional 2FA options. This level of security is sufficient for most users—even when LastPass was breached, the hackers were not able to retrieve anything from users’ password vaults.

KeePass bypasses the concern of storing your passwords online by storing them locally, on your own computer or network. If you decide to use a syncing service like Dropbox to make them available on your other devices, choose one that uses security practices and policies you’re comfortable with.

Like LastPass, KeePass encrypts your vault. You can unlock it using either a master password, key file, or both.

Winner: Tie. LastPass takes strong security precautions to protect your data on the cloud. KeePass keeps your passwords securely encrypted on your own computer. If you need to synchronize them onto other devices, any security concerns now move to the syncing service you choose.

5. Password Sharing

Instead of sharing passwords on a scrap of paper or a text message, do it securely using a password manager. The other person will need to use the same one as you do, but their passwords will be automatically updated automatically if you change them, and you’ll be able to share the login without them actually knowing the password.

All LastPass plans allow you to share passwords, including the free one. The Sharing Center shows you at a glance which passwords you’ve shared with others, and which they’ve shared with you.

If you’re paying for LastPass, you can share entire folders and manage who has access. You could have a Family folder to which you invite family members and folders for each team you share passwords with. Then, to share a password, you’d just add it to the right folder.

KeePass takes an entirely different approach. It’s a multi-user application, so if you store your vault on a shared network drive or file server, others can access the same database using your master password or key file.

This isn’t as finely grained as with LastPass—you choose to share everything or nothing. You could create different password databases for different purposes, and only share your password for certain ones, but this is far less convenient than LastPass’s approach.

Winner: LastPass. It allows you to share passwords and (if you pay) folders of passwords with others.

6. Web Form Filling

Besides filling in passwords, LastPass can automatically fill in web forms, including payments. Its Addresses section stores your personal information that will be filled in automatically when making purchases and creating new accounts—even when using the free plan.

The same goes for the Payment Cards and Bank Accounts sections.

When you need to fill in a form, LastPass offers to do it for you.

KeePass can’t fill in forms by default, but third parties have created plugins that can. A quick search on the KeePass Plugins and Extensions page finds at least three solutions: KeeForm, KeePasser, and WebAutoType. I haven’t tried them, but from what I can tell, they don’t seem to do the job as conveniently as LastPass.

Winner: LastPass. It can fill in web forms natively and seems more convenient than KeePass’s form-filling plugins.

7. Private Documents and Information

Since password managers provide a secure place in the cloud for your passwords, why not store other personal and sensitive information there as well? LastPass offers a Notes section where you can store your private information. Think of it as a digital notebook that’s password-protected where you can store sensitive information such as social security numbers, passport numbers, and the combination to your safe or alarm.

You can attach files to these notes (as well as addresses, payment cards, and bank accounts, but not passwords). Free users are allocated 50 MB for file attachments, and Premium users have 1 GB. To upload attachments using a web browser you will have had to have installed the “binary enabled” LastPass Universal Installer for your operating system.

Finally, there’s a wide range of other personal data types that can be added to LastPass, such as driver’s licenses, passports, social security numbers, database and server logins, and software licenses.

Winner: LastPass. It allows you to store secure notes, a wide range of data types, and files.

8. Security Audit

From time to time, a web service that you use will be hacked, and your password compromised. That’s a great time to change your password! But how do you know when that happens? It’s hard to keep track of so many logins, but many password managers will let you know, and LastPass’ Security Challenge feature is a good example.

It will go through all of your passwords looking for security concerns including:

compromised passwords,

weak passwords,

reused passwords, and

old passwords.

LastPass will even offer to automatically change the passwords of some sites for you, which is incredibly handy, and even available to those using the free plan.

KeePass doesn’t have anything comparable. The best I could find is a Password Quality Estimation plugin that adds a column to rank your password strength, helping you identify weak passwords.

Winner: LastPass. It warns you of password-related security concerns, including when a site you use has been breached, and also offers to change passwords automatically, though not all sites are supported.

9. Pricing & Value

Most password managers have subscriptions that cost $35-40/month. These two apps go against the grain by allowing you to manage your passwords for free.

KeePass is completely free, with no strings attached. LastPass offers a very usable free plan—one that allows you to sync an unlimited number of passwords to an unlimited number of devices, as well as most of the features you’ll need. It also offers additional plans that require you to pay a subscription:

Premium: $36/year,

Families (6 family members included): $48/year,

Team: $48/user/year,

Business: up to $96/user/year.

Winner: Tie. KeePass is completely free, and LastPass offers an excellent free plan.

Final Verdict

Unless you’re a geek, I strongly recommend you choose LastPass over KeePass. I’m familiar with open source software—I used Linux as my only operating system for almost a decade (and loved it)—so I understand that there’s a certain satisfaction that comes from solving technical puzzles to get an app to behave the way you want. But most people don’t feel that way.

LastPass is much more usable and much more capable. It will make your passwords available on all of your devices without needing to resort to a third-party solution. It will also let you share your passwords with others, manage sensitive documents and information, offers full-featured password auditing, and offers to change your passwords automatically.

KeePass has a place for technical users who are willing to put in the effort to get it working the way they want. Some users will appreciate that your data is stored securely on your own computer rather than the cloud, others will love how customizable and extensible it is, and many will appreciate that it’s open source.

LastPass or KeePass, which one is right for you? I think that for most of you the decision is pretty cut and dry. But if you’re having trouble deciding, I recommend you carefully evaluate each app to see for yourself which best meets your needs.

Top 10 Reasons Why You Should Invest In Robotics In 2023

The greatest method to utilize both parties’ distinct strengths is to use robots as partners who work for people in human situations. Robotics in 2023 will be the biggest trend and in this growing market it’s best to invest for higher profits in the future so moving forward we’ll discuss the reason why you should invest in robotics.

1. Lower Operational Expenses

Robotics provides you the ability to cut both direct and overhead expenses, greatly improving your competitiveness. Take energy as an illustration. Robots can lower your energy costs since they don’t require a minimum level of lighting or heating. According to current estimates, there might be an 8% savings for every 1°C decrease in heating levels, and needless lighting can result in savings of up to 20%.

2. Enhance the Consistency and Quality of the Product

A consistently high-quality finish is possible with robots since they don’t experience the consequences of weariness, attention, or repeated and tiresome jobs. They are programmed to obey instructions, and their inherent precision and reproducibility guarantee a high-quality finish for every product created!

3. Boost the Productivity of Workers

Because employees are no longer required to labor in hot or possibly dangerous areas, the introduction of robots can enhance working conditions for personnel. They can also get useful programming skills and engage in more interesting employment.

4. Boost Manufacturing Output Rates

Robots can operate unsupervised day, night, and on the weekends, allowing for higher productivity levels to meet order deadlines. There is no downtime, illness, or focus lapses with a robotic solution. Programming robots to handle new goods offline also increases flexibility while assuring a speedier overall output.

5. Increase Flexibility in Product Manufacturing

The production line can benefit from flexibility thanks to robots. To get the most out of your investment, use robotics equipment for a range of items once the necessary procedures have been put into the robot controller.

6. Lower Material Waste and Boost Output

Robotics significantly improve product quality. The quantity of breakages and waste created as a result of subpar or irregular finishing decreases when more goods are completed at the first try to the standard expected by consumers. Greater yields result from more completed items being produced.

7. Boost Workplace Wellness and Security

Tasks presently performed by manual employees that are unpleasant, difficult, or dangerous to their health can be easily performed by robots. Robotics considerably reduces the risk of mishaps brought by coming into touch with machine tools or other potentially dangerous manufacturing equipment. Additionally, they aid in the eradication of conditions like repetitive strain injuries that are linked to rhythmic or demanding activities (RSI).

8. Reduce Staff Turnover and Hiring Challenges

The greatest levels of expertise and training are needed to achieve the high levels of accuracy that today’s industrial processes demand. Robots provide a perfect substitute for highly trained manual labor, who are getting harder and more costly to hire. Once they have been programmed, there are no expenses involved with hiring new employees or keeping existing ones trained. Additionally, robots provide more flexibility in terms of work schedules and the capacity to manage various production activities.

9. Lower Capital Expenses

Robots can cut down on waste and the cost of consumables. Businesses can forecast the manufacturing rate with confidence and guarantee a quick and effective service by getting items through the production process more quickly.

10. Make High-Value Production Areas More Compact

Top 10 Cryptocurrency Predictions That Investors Should Follow In 2023

There are as many cryptocurrency predictions as there are analysts charting the future of crypto

We have been witnessing massive changes in the cryptocurrency market over the past decade. While the year 2023 is coming to an end, crypto traders around the world are experiencing their worst nightmares with FTX collapses and a decline in the crypto market. However, if we take a look at the cryptocurrency predictions for 2023, we could say the market will recover and that now is probably the best time to invest. Will cryptocurrency soar, plummet, or tease investors along an unpredictable path for the foreseeable future? Will Bitcoin continue its volatility? Will regulation play a more significant role? Which type of cryptocurrency will be the best bet this coming year? In this article, we have listed the top 10 cryptocurrency predictions that investors should follow in 2023. 

A Lot More Investors Will Adopt Bitcoin 

Crypto prediction: According to The Ascent, Ric Edelman, founder of the Digital Assets Council of Financial Professionals, predicts that more than 500 million people worldwide will own Bitcoin by the end of 2023. CoinTelegraph, BlockFi co-founder Flori Marquez agrees, crediting regulatory clarity and improved understanding of the industry for helping drive greater adoption. It is one of the top 10 cryptocurrency predictions that investors should follow in 2023. 

Past Performance of the Crypto Market Suggest 2023 Will Be a Good Year

If we look at the performance of the crypto market in the past years, we can see that the whole market is following Bitcoin’s four-year cycle. In this four-year cycle, we can see that the market has a bullish year, followed by a correction year (when it is down), followed by a good, then excellent year. 2023 was a bullish year, while 2023 was a year of crypto winter. This suggests next 2023 will be a good year followed by an even better one. It is one of the cryptocurrency predictions for 2023.

The First Spot Bitcoin ETF Could Get Approved 

Some crypto investors predict that the first spot Bitcoin exchange-traded fund (ETF) in the United States could be approved this year, giving investors direct exposure to the cryptocurrency itself. The Securities and Exchange Commission allowed the launch of ProShares’ Bitcoin Strategy ETF last year, but that just tracks Bitcoin futures contracts. However, because the market is now large and mature enough to support it, analysts believe a Bitcoin Spot ETF will be approved.

Bear Market Will Be Over at the Beginning of 2023

Some experts predict that we’re currently experiencing a crypto decline, which will probably hit its bottom by the end of 2023. These predictions are also made by the past performance of the crypto market, which suggests that in the next three months, the bear market will come to an end. It is one of the top 10 cryptocurrency predictions that investors should follow in 2023.

Moving Toward Decentralized Finance (DeFi)

Emerging crypto developments such as decentralized finance (DeFi) and decentralized autonomous organizations (DAOs) are “likely to be the highest growth areas of crypto,” believes Bryan Gross, network steward at crypto platform ICHI. DeFi aims to recreate traditional financial products without middlemen, while DAOs could be considered a new internet community. It is one of the cryptocurrency predictions for 2023.

Once the Crypto Market Is at the Bottom, a Bull Market Will Happen

Every time the crypto market experienced a bear market, it was always followed by a bull run market, and there is no reason why this wouldn’t happen again. Even though investors are currently in fear, those who manage to stick through or even invest now will be rewarded when the market eventually explodes again. It is one of the top 10 cryptocurrency predictions that investors should follow in 2023.

Ether Will Outperform Bitcoin Again

Ether outperformed Bitcoin in 2023, gaining 418 percent compared to 66 percent for Bitcoin. Analysts believe either will continue to do well because of the surge in NFT sales volumes. Most of these tokens still run on the ethereum blockchain.

Bitcoin Will Come Back

Experts have noticed that BTC always performs in a four-year cycle, as we mentioned before, and there is no doubt that the performance of BTC will influence the whole market. If you take a look at the chart below, we can see that BTC has ups and downs but that it always recovers. According to CryptoPredictions, BTC will start 2023 at a 2% up in price and will continue this trend throughout the year. It is one of the cryptocurrency predictions for 2023.

Most meme coins will disappear

Last year, a Dogecoin spinoff Shiba Inu climbed 44,540,000 percent. Squid, a coin named for the television drama “Squid Game,” jumped more than 75,000 percent in less than a week — only to disappear soon after. It is one of the top 10 cryptocurrency predictions that investors should follow in 2023.

Web3 Will Become the Next Big Thing

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