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Bit stuffing is a technique used in data communication systems to detect and correct errors that may occur during the transmission of data. It works by adding extra bits to the data being transmitted in order to flag when an error has occurred.

One common way to implement bit stuffing in Java is to use a flag byte (such as 0x7E) to indicate the start and end of a frame, and to use a special escape byte (such as 0x7D) to indicate that the next byte is a stuffed bit. For example, the sender would add a stuffed bit before every occurrence of the flag byte within the data being transmitted, so that the flag byte will not be mistaken for the start or end of a frame at the receiver.

Here’s an example of how you could implement bit stuffing in Java −

public static byte[] bitStuff(byte[] data) { final byte FLAG = 0x7E; final byte ESCAPE = 0x7D; byte[] stuffedData = new byte[data.length * 2]; int stuffedIndex = 0; for (int i = 0; i < data.length; i++) { byte b = data[i]; stuffedData[stuffedIndex++] = ESCAPE; stuffedData[stuffedIndex++] = (byte) (b ^ 0x20); } else { stuffedData[stuffedIndex++] = b; } } return stuffedData; }

At the receiver side , you can use the similar concept to retrieve the original data.

public static byte[] bitUnStuff(byte[] data) { final byte FLAG = 0x7E; final byte ESCAPE = 0x7D; byte[] unstuffedData = new byte[data.length]; int unstuffedIndex = 0; for (int i = 0; i < data.length; i++) { byte b = data[i]; if (b == ESCAPE) { unstuffedData[unstuffedIndex++] = (byte) (data[++i] ^ 0x20); } else { unstuffedData[unstuffedIndex++] = b; } } return unstuffedData; }

This is a basic example of bit stuffing technique, It can be enhanced to handle more error cases and also validate the data using CRC or Checksum.


Sure! Here’s an example of how you could use the bitStuff() and bitUnStuff() methods in a simple program −

public static void main(String[] args) { byte[] data = {0x48, 0x65, 0x6C, 0x6C, 0x6F, 0x7E}; byte[] stuffedData = bitStuff(data); System.out.println("Original Data: "+Arrays.toString(data)); System.out.println("Stuffed Data: "+ Arrays.toString(stuffedData)); byte[] unstuffedData = bitUnStuff(stuffedData); System.out.println("Unstuffed Data: "+ Arrays.toString(unstuffedData)); }

When you run this program, it will first call the bitStuff() method to stuff the original data, then print out the original data and the stuffed data.

Then it will call the bitUnStuff() method to retrieve the original data, then it will print the unstuffed data.


For the given example of data

0x48, 0x65, 0x6C, 0x6C, 0x6F, 0x7E, Original Data: [72, 101, 108, 108, 111, 126] Stuffed Data: [72, 101, 108, 108, 111, 93, 30, 126] Unstuffed Data: [72, 101, 108, 108, 111, 126]

You can see the stuffed data has an extra byte 93, 30 which is the stuffed version of 7E.

Also you can see the unstuffed data is same as the original data, which confirms that the data is retrived successfully without any errors.

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Using Apache Flink With Java

This article was published as a part of the Data Science Blogathon.


Apache Flink is a big data framework that allows programmers API concepts and standard data transformations available in the Apache Flink Java API. The fluid style of this API makes it easy to work with Flink’s central construct – a distributed collection. First, we’ll look at Flink’s DataSet API transformations and use them to implement a word-counting program. Then we’ll take a brief look at Flink’s DataStream API, which allows you to process event streams in real time.

Dependency on Maven

To get started, we’ll need to add the Maven dependencies to the link-java and flink-test-utils libraries:

org.apache.flink flink-java 1.2.0 org.apache.flink flink-test-utils_2.10 1.2.0 test Basic API Concepts

When working with Flink, we need to know a few things related to its API: Various data transformation functions are available, including filtering, mapping, joining, grouping, and aggregation sink operation in Flink initiates the execution of a stream to produce the desired result of the program, such as saving the result to the file system or printing it to standard output. Flink transformations are lazy and not executed until the sink operation is invoked.

The  API has two modes of operation, i.e., batch and real-time. If you’re dealing with a limited data source that can be processed in batch mode, you’ll use the DataSet API. To process unlimited data streams in real-time, you must use the DataStream API.

DataSet API Transformation

Let’s create an ExecutionEnvironment to start processing:

ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

Note that running the application on the local machine will do the processing on the local JVM. If you wanted to start processing on a cluster of machines, you would need to install Apache Flink and configure the ExecutionEnvironment accordingly.

Create a dataset

We need to supply data to our program to perform data transformations.

DataSet amounts = chúng tôi elements(1, 29, 40, 50);

You can create a DataSet from multiple sources, such as Apache Kafka, CSV, a file, or virtually any other data source.

Filter and reduce

Let’s say you want to filter out numbers above a certain threshold and then sum them all. You can use filter() and reduce() transformations to achieve this:

int threshold = 30; List collect = amounts .collect(); claimThis(collect.get(0)).isEqualTo(90);

Note that the collect() method is the sink operation that initiates the actual data transformations.


Let’s say you have a DataSet of Person objects:

private static class Person { private int age; private String name; }

Next, we create a DataSet from these objects:

DataSet personDataSource = chúng tôi collection( Arrays.asList( new Person(23, "Tom"), new Person(75, "Michael")));

Suppose you want to extract only the age field from each collection object. You can only get certain fields of the Person class using the map() transformation:

List ages = personDataSource .collect(); claim this(ages).size(2); assert(ages).contains(23, 75);


When you have two data sets, you might want to join them in some id field. You can use the join() transformation for this.

Let’s create a collection of transactions and user addresses:

Tuple3 address = new Tuple3(1, "5th Avenue", "London"); = chúng tôi elements(address); Tuple2 first transaction = new Tuple2(1, "Transaction_1"); = chúng tôi elements(first transaction, new Tuple2(12, "Transaction_2"));

The first field in both tuples is of type Integer and is the id field on which we want to join the two datasets.

To perform the actual connection logic, we need to implement the KeySelector interface for the address and transaction:

private static class IdKeySelectorTransaction @Overwrite public Integer getKey(Tuple2 value) { return value.f0; } } private static class IdKeySelectorAddress @Overwrite public Integer getKey(Tuple3 value) { return value.f0; } }

Each selector returns only the field on which the join is to be performed.

Unfortunately, it’s not possible to use lambda expressions here because Flink needs generic type information.

free star

Next, let’s implement the merge logic using these selectors:

connected = transaction.connect(addresses) .where(new IdKeySelectorTransaction()) .equalTo(new IdKeySelectorAddress()) .collect(); claim this(joined).hasSize(1); claim that(joined).contains(new Tuple2(first transaction, address));


Let’s say you have the following Tuple2 collection:

Tuple2 secondPerson = new Tuple2(4, "Tom"); Tuple2 third person = new Tuple2(5, "Scott"); Tuple2 fourth person = new Tuple2(200, "Michael"); Tuple2 firstPerson = new Tuple2(1, "Jack"); fourth person, second person, third person, first-person);

If you want to sort this collection by the first field of the tuple, you can use the sortPartitions() transform:

.sortPartition(new IdKeySelectorTransaction(), Order.ASCENDING) .collect(); claim that (sorted) .containsExactly(first person, second-person, third-person, fourth person);

Word Count

The word count problem is a problem commonly used to demonstrate the capabilities of big data processing frameworks. The basic solution involves counting the occurrences of words in the text input.

As the first step in our solution, we create a LineSplitter class that splits our input into tokens (words), collecting a Tuple2 of key-value pairs for each token. In each of these tuples, the key is a word found in the text, and the value is an integer (1).

This class implements the FlatMapFunction interface, which takes a string as input and creates a Tuple2:

@Overwrite Stream.of(value.toLowerCase().split(“W+”)) } }

We call the collect() method on the Collector class to push the data forward in the processing process.

Our next and final step is to group the tuples by their first elements (words) and then perform a sum on the second element to produce the number of occurrences of the words:

ExecutionEnvironment env, List lines) throws Exception { DataSet text = env.fromCollection(lines); return text.flatMap(new LineSplitter()) .groupBy(0) .aggregate(Aggregations.SUM, 1); }

We use three types of Flink transformations: flatMap(), groupBy(), and aggregate().

Let’s write a test to confirm that the word count implementation works as expected:

List lines = Arrays.asList( "This is the first sentence", "This is the second one-word sentence"); assert that(collect).containsExactlyInAnyOrder( new Tuple2("a", 3), new Tuple2("sentence", 2), new Tuple2("word", 1), new Tuple2("is", 2), new Tuple2("that", 2), new Tuple2("other", 1), new Tuple2("first", 1), new Tuple2("with", 1), new Tuple2("one", 1)); DataStream API

Creating a data stream

Apache Flink also supports event stream processing through the DataStream API. If we want to start consuming events, we must first use the StreamExecutionEnvironment class:

StreamExecutionEnvironment execution environment = StreamExecutionEnvironment.getExecutionEnvironment();

Furthermore, we can create a stream of events using the runtime from various sources. It could be some message bus like Apache Kafka, but in this example, we simply create a feed from a few string elements:

DataStream dataStream = execution chúng tôi elements( "This is the first sentence", "This is the second one-word sentence");

We can apply transformations to each element of the DataStream as in a normal DataSet class:

SingleOutputStreamOperator uppercase =;

To trigger the execution, we need to call a sink operation like print(), which just prints the result of the transformations to standard output, followed by the execute() method in the StreamExecutionEnvironment class:

uppercase.print(); env.execute();

It produces the following output:

Events window

When processing a real-time stream of events, it may sometimes be necessary to group events and apply some calculation to a window of those events.

Suppose we have a stream of events, where each event is a pair consisting of an event number and a timestamp of when the event was sent to our system, and that we can tolerate events that are out of sequence, but only if they are not more than twenty seconds late.

In this example, first, create a stream simulating two events that are several minutes apart, and define a timestamp extractor that determines our delay threshold:

= chúng tôi elements( new Tuple2(16,, new Tuple2(15, .assignTimestampsAndWatermarks( new BoundedOutOfOrdernessTimestampExtractor @Overwrite public long extract timestamp(Tuple2 element) { return element.f1 * 1000; } });

Next, we define a window operation to group our events into five-second windows and apply a transformation to those events:

.windowAll(TumblingEventTimeWindows.of(Time.seconds(5))) .maxBy(0, true); reduced.print();

It gets the last element of each five-second window, so it prints:

We don’t see the second event because it arrived later than the specified delay threshold.


In this article, we introduced the Apache Flink framework and looked at some of the transformations that come with its API.

We implemented a word count program using Flink’s smooth and functional DataSet API. We then looked at the DataStream API nted a simple real-time transformation to an event stream.

The implementation of all these examples and code snippets can be found on GitHub – this is a Maven project, so it should be easy to import and run as is.

Flink transformations are lazy and not executed until the sink operation is invoked. The  API has two modes of operations, i.e., batch and real-time. If you’re dealing with a limited data source that can be processed in batch mode, you’ll use the DataSet API.

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How To Run 32 Bit Programs On 64 Bit Windows

64-bit programs run faster and more efficiently than 32-bit applications. Any reasonably modern PC has a 64-bit processor. But, how do you run 32-bit software on a 64-bit computer?

Modern computers—those manufactured in the last several years—are powered by 64-bit processors and operating systems and are only natively capable of running 64-bit applications. This is why software shipped these days is almost exclusively 64-bit. You will still run into some 32-bit apps (especially if you’re running older apps), and running them on a 64-bit version of Windows is usually pretty seamless. So, how does it all work? Let’s find out.

Table of Contents

Can You Directly Run 32-bit Software On a 64-bit Machine?

Understanding how a 64-bit architecture differs from a 32-bit system is a complicated topic that is beyond the scope of this article. Just know that a 64-bit processor (and OS) doesn’t just process more information at once but does so in a radically different manner than a system with older architecture.

So while apps designed for a 32-bit computer might appear to run just the same on a 64-bit machine, there is more going on under the hood than just changing the compatibility mode. The environment expected by a 32-bit app doesn’t exist in a 64-bit version of Windows, which makes it impossible for such an application to interface with the hardware directly.

The fix? Emulation. The only way to get a 32-bit program working is to emulate the old-school architecture and provide the app with the same interface it is built for.

The Default Option: WOW64

WOW64 is a Windows subsystem designed to run 32-applications on a 64-bit machine. WOW64 simulates the environment of a 32-bit operating system, providing older applications with the interface found in previous Windows versions.

An Expensive Alternative: Virtualization

Virtual Machines are a well-known method of running different architectures and operating systems on hardware not designed to support it. You can install and run apps meant for Linux or Apple’s macOS on a Windows PC with an Intel processor without any drastic changes.

You can use the same technique to run an older, 32-bit version of Windows on your modern PC. This will let you run legacy applications on your system even if your current processor is 64-bit.

Remember, though, that this method involves a lot of work and is frankly unnecessary. It is much easier to use the built-in WOW64 emulator than to hunt for a copy of 32-bit Windows XP.

Installing 32-bit Apps On a 64-bit Computer

There is no difference in installing a 32-bit application on a 32-bit OS versus a 64-bit version of Windows. Whether you have a CD-ROM or a setup file, you just run the installation and let the operating system sort it out.

Windows deals with 32-bit versions of programs by putting them in a different directory. There is the standard Program Files folder, which holds all 64-bit software you have installed, and a Program Files (x86) that is home to apps meant for a 32-bit machine.

The software present in the x86 directory is run by emulating a 32-bit version of Windows using WOW64. This process is completely automatic, so you can run apps present in both Program Files without any difference.

Can You Run 32-bit Apps On a 64-bit CPU?

Many people believe that old programs can only run on a 32-bit processor. While it is true that only a 32-bit computer can natively run these apps, all modern system types can run such programs just as well.

For most users, this boils down to simply running the said app, as Windows will take care of the technical details of emulating it through WOW64 by itself. If you want to take a different approach (perhaps if the utility isn’t working for you), you can use virtualization.

Anomaly Detection Model On Time Series Data In Python Using Facebook Prophet

This article was published as a part of the Data Science Blogathon


Time series data is the collection of data at specific time intervals like on an hourly basis, weekly basis. Stock market data, e-commerce sales data is perfect example of time-series data. Time-series data analysis is different from usual data analysis because you can split and create samples according to randomness in data analysis and preprocessing. Still, the next value depends on the previous input in time series data, so its analysis and preprocessing should be done with care. We use different visualization techniques to identify a hidden pattern in data but anomaly detection in time-series data has a different model technique. This article will learn and perform hands-on practise on detecting anomalies in time-series data using Facebook Prophet.

What is Anomaly Detection?

Global outliers are the data points far apart from the normal distribution of data—far outside means which exist far outside the entirety of the dataset. We can identify global outlies with naked eyes, which can occur due to business processing issues or data ingesting problems. It can also be a natural data element where business processes are generated, so it is essential to understand the business context while working with anomalies.

Contextual outliers are very common in time-series data. These values exist within the global expectation but may appear anomalous within specific seasonal patterns, so the way has a trend.

I know these terms might seem a bit complex to understand, but when we work with data, we will learn how to find global and contextual outliers and visualize them in time-series data.

Seasonality is an essential component of time series which indicates a regular and predictable increase and decrease in amount according to calendar year.

The trend is another component of time series which means any pattern in data that shows movement in time series data that may be increasing or decreasing.

Now we have a glance at anomalies in data, what kind of anomalies can occur, and why it is essential to find and deal with them. So the remaining things we will learn with practice so let’s make our hands dirty with loading and performing anomaly detection on a real dataset.

Brief on Dataset

The dataset we will use is named New York Taxi dataset. The dataset is straightforward and contains only two columns as timestamp and count of taxi trips, and there is a timestamp for every half-hour interval. The data spans over seven months, from July 2014 to Jan 2024. The target is to predict how many taxis were active daily or on half an hour basis in new york city. A dataset is readily available on Kaggle, and you can download it from here. We aim to detect and visualize anomalies in the dataset.

Hands-on Anomaly Detection

Importing Libraries

We are importing the basic processing libraries and visualizing Numpy and pandas for data wrangling tasks. We will use plotly express to imagine the time-series data because it makes easy visualization of a lot of data points, and it allows you to zoom and visualize perfectly a single data point.

import pandas as pd import numpy as np import matplotlib.pyplot as plt from datetime import datetime import as px import matplotlib as mpl mpl.rcParams['figure.figsize'] = (10, 8) mpl.rcParams['axes.grid'] = False df = pd.read_csv("nyc_taxi.csv") df.shape

The data has 10320 rows and only 2 columns.


df['timestamp'] = pd.to_datetime(df['timestamp'])

Now we have 10320 data points, so to visualize it quickly, I am resampling the data hourly. By keeping timestamp as an index, we are resampling it on an hourly basis from half an hour and taking its mean. It will not disturb the data distribution because it takes 2 values and finds its mean.

df = df.set_index('timestamp').resample('H').mean().reset_index() df.shape

After performing the above processing, the shape of data will behalf.

Data Visualization

Now we are going to plot the data, and I am drawing the line chart. On the x-axis, we have a timestamp, and on Y-axis, we have values.

#express to plot entire data fig = px.line(df.reset_index(), x='timestamp', y='value', title='NYC Taxi Demand') #slider fig.update_xaxes( rangeslider_visible = True, rangeselector = dict( buttons = list([ dict(count=1, label='1y', step="year", stepmode="backward"), dict(count=2, label='2y', step="year", stepmode="backward"), dict(count=2, label='5y', step="year", stepmode="backward") ]) ) )


we are using Facebook Prophet to detect anomalies in time-series data. So first, we will install the library.

!pip install fbprophet from fbprophet import Prophet taxi_df = df.reset_index()[['timestamp', 'value']].rename({'timestamp':'ds', 'value':'y'}, axis='columns')

Separate the train and test set

#train test split

From 2014 July to 27th January 2024, we have taken in the train set and remain in the test set.

Create Prophet model

When we use Facebook prophet, it gives the output according to the confidence interval. By default, it is set to 80 per cent, and we change it to a 95 per cent confidence interval so it will give us prediction and lower and upper confidence intervals. After that, we feed the train data to it. After running the below snippet, it shows you that yearly seasonality is not valid because data is of only six months.

m = Prophet(changepoint_range=0.95)

Forecast on test data

We are creating a dataframe that only consists of dates of test data in timestamp format to forecast test data. We need to create a dataframe with hourly frequency because by default it creates on daily basis.

future = m.make_future_dataframe(periods=119, freq='H')

Now we will predict the target value on these dates. you can see that that is the predicted value, that lower is the lower confidence interval and the upper column represent the upper confidence interval.

forecast = m.predict(future) forecast[['ds','yhat','yhat_lower','yhat_upper']].tail()

Now we want to see the difference between actual and predicted values so I am creating a new dataframe where we merge the actual and forecasted dataframe. At the same time, we also visualize the forecast values to understand the predictions.

result = pd.concat([taxi_df.set_index('ds')['y'], forecast.set_index('ds')[['yhat','yhat_lower','yhat_upper']]], axis=1) fig1 = m.plot(forecast)

The black points are the actual outcomes(target), dark blue points are the predicted points. upper light blue shades represent the upper confidence interval and lower shades represent the lower confidence interval. Still, we have not done anything with outliers but some points we can see as outliers which we will deal with in the next section.

We will also plot the component of time series data. what these components will give? It will take the time-series data and give the trend and seasonality component out of it.

comp = m.plot_components(forecast)

The top one is a trend. Data from 2014 July has an increasing trend and then it has a decreasing trend.

The second plot shows the weekly trend then the number of rides running in a new york city decrease at Sunday time and starts increasing from Monday. Basically, on Sunday everyone usually takes holiday and from Monday offices are about to start.

The third plot is about the daily pattern which shows the 24 hours window. the number of rides is slower from midnight to morning 4’O clock and then increases till 8 pm and then reduces to some extent.

why this component’s visualization is important? The reason is when I spoke about contextual outliers then it will take a weekly and daily seasonality into consideration while modelling so that it can detect outliers that look like inliers.

Anomaly and outlier detection

First, we are adding two columns to the result dataframe. error is a difference between actual and predicted values. The second is uncertainty level which is the difference between upper and lower confidence intervals.

result['error'] = result['y'] - result['yhat'] result['uncertainty'] = result['yhat_upper'] - result['yhat_lower']

The error can be negative or positive so we are taking absolute of it and checking that is it greater than the uncertainty level then most probably it is an outlier or an incident that is most likely to outperform in a dataset from a normal distribution. And these points or records we will get it will be assigned as an anomaly in the data.

Among 5160 rows only a few are detected as an anomaly. let us see it in brief. The first record on 2nd November 2014 in new york is a marathon so most likely people around different cities come to become a part of it and enjoy it. After that we can see on 1st January it is new year eve so all the normal plots we have seen upper are reverse on this dates because as we saw that from midnight to 4 AM in morning number of taxi are less active but on new year eve, it is completely reverse.

Visualizing the Anomalies in Data

Now we are creating a scatter plot in which the x-axis is a timestamp, the y-axis values, and color of points vary as per anomaly. the color is the anomaly part of it so let us see that how anomalies look in a graph.

#visualize the anomaly data fig = px.scatter(result.reset_index(), x='ds', y='y', color='anomaly', title='NYC Taxi Demand') #slider fig.update_xaxes( rangeslider_visible = True, rangeselector = dict( buttons = list([ dict(count=1, label='1y', step="year", stepmode="backward"), dict(count=2, label='3y', step="year", stepmode="backward"), dict(count=2, label='5y', step="year", stepmode="backward"), dict(step="all") ]) ) ) Conclusion

Anomalies in the data can be present in different forms which deviate and has an inverse or completely reverse behavior than actual data. Detecting anomalies depend on your business use case and domain that how and what type of cases you assume to happen as per seasonality and which case you consider the uncritical situation in business. In most businesses, these points are very helpful to drive some strategies and to think in another way. Hence when you are working with time-series data then it is important to take care of all these components.

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What Are The Pros And Cons Of Using Python Vs. Java?

In this article, we will learn the pros and cons of using Python vs. Java.

Pros of Java

Simple − Java is a must-know programming language due of its simplicity. Because it is C++-based and uses automated garbage collection, we don’t have to worry about freeing up memory for things that are no longer being used. To further simplify Java for both reading and writing, features such as explicit pointers and operator overloading have been removed.

Object-Oriented − As an Object-Oriented Programming Language, Java has many useful features such as Data Encapsulation, Inheritance, Data Hiding, and so on. As a result, Java is a good language for mapping real-world entities into objects and solving real-world issues.

Platform Independent − The compilation of code in Java is not platform-specific, but rather occurs as platform-independent bytecode. After that, the Java Virtual Machine (JVM) interprets it. There is no OS needed for running the software. This guarantees that your code will operate on Mac, Windows, Linux, and any other platform that supports the Java Virtual Machine. As a consequence, we can reach more people. It follows the Write once, run anywhere principle.

Secure − It assists developers in creating safe and tamper-proof code by utilizing public-key encryption.

Robust − Strong memory management is one of the reasons Java is such a stable programming language. Java code may also be used to deal with errors. To further strengthen our code’s safety, we may additionally use type-checking. Since it does not make use of explicit pointers, programs cannot do direct memory access.

Distributed Computing − Java’s support for distributed computing stems from the language’s inclusion of many APIs for establishing connections to external resources, such as CORBA and RMI.

Cons of Using Java Memory management

Java’s built-in support for managing memory helps to speed up the development process. The efficiency and precision of garbage collection may likely drop to the point where it is equal to human work. Because of this, Java applications rely heavily on in-memory processing and manipulation.

Code readability

Java applications are subject to being lengthy because of the complexity of their extensive code courses. If the developer has not given sufficient documentation and notes, understanding and analysing the system may take some time.


When compared to other languages, Java necessitates a large amount of memory space.

As there are high memory and processing requirements, so does the cost of hardware increase.


Every time Java code is executed, it is interpreted by the Java Virtual Machine (JVM). Consequently, productivity falls. Data processing in real-time is currently not possible with Java.

Garbage collection

When it comes to garbage collection, Java enables automated garbage collection over which the programmer has no say. Memory-freeing methods like delete() and free() are not included. Java’s merits, which include being platform-independent, secure, and robust, have helped to keep it one of the most popular programming languages despite these drawbacks.

Pros of Python

Easy and short Syntax − The syntax is simple and thus easily picked up by programmers.

Expressive Language − Small snippets of code can be used to finish large lines of code.

Cross-Platform Language − Works across all operating systems.

Smooth Learning Curve − Python is a very accessible programming language that is typically introduced to students as a first programming language course. This tool lets you put a limit on the way a developer thinks by forcing them to concentrate on the most basic principles and building blocks of their skill.

Free and Open Source − Python is a free and open-source programming language that may be accessed from anywhere worldwide.

Vast Standard Library − The offerings of these libraries, such as MatPlotLib, Pandas, Request, NumPy, and others, are vast and make the task of a developer quite simple.

Flexible with other languages and tools − Python is a versatile programming language that can be readily integrated with a wide range of tools and frameworks to handle a wide range of problems.

Versatility combined with a vast toolkit for practically anything − Python can be used for a wide range of jobs, including data automation, data scientists, data engineers, QA engineers, and DevOps specialists.

High Speed of Development − When it comes to studying and creating Python-based software, the straightforward syntax greatly reduces complexity and increases productivity. Using pre-coded components saves time and effort by providing reusable building blocks for new software projects.

Cons of Using Python

Less Speed − It is slower because it is an interpreted language. Despite Python’s incredible development speed, Java and C++ still dominate it in terms of execution speed. Program execution is slowed down by the interpreter used to inspect and assign variables.

No Multithreading − The Global Interpreter Lock, or GIL, mechanism lies at the core of Python. It only allows the execution of one set of bytecode instructions at once. While limiting the performance of multi-threaded systems created to run numerous workflows simultaneously, GIL enhances the performance of single-threaded programs.

High Memory Consumption − The Python garbage collector delays returning system resources once an item is no longer in use. This causes Python’s memory problems to occur often.

Challenges with front-end and mobile development − Not a single smartphone platform supports the Python programming language. Java is used only for Android app development, whereas Swift and Objective C are used exclusively for iOS app development. Therefore, Python can’t keep up with the growing mobile market and sustain its popularity.

Because of its limitations in mobile computing, it is not employed in app development.

Python’s mobile computing features are weak. As a result, it is not commonly utilized in application development.

Since Python is dynamic, mistakes are displayed at run time. Because no errors are generated at compile time, developers running large chunks of code may lose time.

There is no commercial support.


There are several ways in which Python and Java are equivalent to one another. However, there are a few key areas of variation between the two, including execution speed and constraints, the usage of classes during programming, and a few more.

The functioning and selection of either language are determined by the user’s preferences as well as their accessibility. Although gathering knowledge on your own can be difficult.

Java Program To Find The Sum Of Natural Numbers Using Recursion

In this article, we will understand how to find the sum of natural numbers using recursion. All possible positive numbers from 1 to infinity are called natural numbers. A recursive function is a function that calls itself multiple times until a particular condition is satisfied.

Recursion is the process of repeating items in a self-similar way. In programming languages, if a program allows you to call a function inside the same function, then it is called a recursive call of the function.

Many programming languages implement recursion by means of stacks. Generally, whenever a function (caller) calls another function (callee) or itself as callee, the caller function transfers execution control to the callee. This transfer process may also involve some data to be passed from the caller to the callee.

Below is a demonstration of the same −


Suppose our input is −

Enter the number : 25


The desired output would be −

The sum of natural numbers up to 25 is 325 Algorithm Step 1 - START Step 2 - Declare 2 integer values namely my_input and my_sum. Step 3 - Read the required values from the user/ define the values Step 4 - A recursive function ‘Add’ is defined which takes an integer as input and returns the sum of the input value and its previous value until the input value is reduced to 0. Step 5 - The recursive function is called and the value ‘my_input’ is passed to it. Store the return value. Step 6 - Display the result Step 7 - Stop Example 1

Here, the input is being entered by the user based on a prompt. You can try this example live in ourcoding ground tool .

import java.util.Scanner; public class NaturalNumbers {    public static void main(String[] args) {       int my_input, my_sum;       System.out.println("Required packages have been imported");       Scanner my_scanner = new Scanner(;       System.out.println("A reader object has been defined ");       System.out.print("Enter the number : ");       my_input = my_scanner.nextInt();       System.out.println("The number is defined as " +my_input);       my_sum = Add(my_input);       System.out.println("The sum of natural numbers up to " + my_input + " is " + my_sum);    }    public static int Add(int my_input) {          return my_input + Add(my_input - 1);       else          return my_input;    } } Output Required packages have been imported A reader object has been defined Enter the number : 25 The number is defined as 25 The sum of natural numbers up to 25 is 325 Example 2

Here, the integer has been previously defined, and its value is accessed and displayed on the console.

public class NaturalNumbers {    public static void main(String[] args) {       int my_input, my_sum;       my_input = 25;       System.out.println("The number is defined as " +my_input);       my_sum = Add(my_input);       System.out.println("The sum of natural numbers up to " + my_input + " is " + my_sum);    }    public static int Add(int my_input) {       return my_input + Add(my_input - 1);    else       return my_input;    } } Output The number is defined as 25 The sum of natural numbers up to 25 is 325

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