Skip to main content

How to reverse a String in Java ?


  • A Traditional for Loop 

We know that strings are immutable in Java. An immutable object is an object whose internal state remains constant after it has been entirely created.

Therefore, we cannot reverse a String by modifying it. We need to create another String for this reason.

First, let's see a basic example using a for a loop. We're going to iterate over the String input from the last to the first element and concatenate every character into a new String:

public String reverse(String input) {

    if (input == null) {
        return input;
    }

    String output = "";

    for (int i = input.length() - 1; i >= 0; i--) {
        output = output + input.charAt(i);
    }

    return output;
}

As we can see, we need to be careful at the corner cases and treat them separately.

  • Using A StringBuilder

Java also offers some mechanisms like StringBuilder and StringBuffer that create a mutable sequence of characters. These objects have a reverse() method that helps us achieve the desired result.

Here, we need to create a StringBuilder from the String input and then call the reverse() method:

public String reverseUsingStringBuilder(String input) {
    if (input == null) {
        return null;
    }

    StringBuilder output = new StringBuilder(input).reverse();
    return output.toString();
}

  • Reversing the Order of Words in a Sentence

Now, let's assume we have a sentence with words separated by spaces and no punctuation marks. We need to reverse the order of words in this sentence.

We can solve this problem in two steps: splitting the sentence by the space delimiter and then concatenating the words in reverse order.

First, we'll show a classic approach. We're going to use the String.split() method in order to fulfill the first part of our problem. Next, we'll iterate backward through the resulting array and concatenate the words using a StringBuilder. Of course, we also need to add a space between these words:

public String reverseTheOrderOfWords(String sentence) {
    if (sentence == null) {
        return null;
    }

    StringBuilder output = new StringBuilder();
    String[] words = sentence.split(" ");

    for (int i = words.length - 1; i >= 0; i--) {
        output.append(words[i]);
        output.append(" ");
    }

    return output.toString().trim();
}

Comments

Popular posts from this blog

Complete Tutorial on Business Analytics

CRISP-DM (Cross Industry Standard Process for Data Mining)                                    The framework is made up of 6 steps: Business Issue Understanding Data Understanding Data Preparation Analysis/Modeling Validation Presentation/Visualization The map outlines two main scenarios for a business problem: Data analysis Predictive analysis Data analysis refers to the more standard approaches of blending together data and reporting on trends and statistics and helps answer business questions that involve understanding more about the dataset such as "On average, how many people order coffee and a donut per transaction in my store in any given week?" Predictive analysis will help businesses predict future behavior based on existing data such as "Given the average coffee order, how much coffee can I expect to sell next week if I were to add a new brand of coffee?" Business Issue Understanding "This initial phase focuses on understanding the project objectives a

What is a Type II Error?

  A Type II error is a false negative in a test outcome, where something is falsely inferred to not exist. This usually means incorrectly accepting the null hypothesis (H0), which is the testing statement that whatever is being studied has no statistically significant effect on the problem. An example would be a drug trial that incorrectly concludes the prescribed medication had no effect on the patient’s ailment, when in fact the disease was cured, but subsequent exams caused a false positive showing the patient was still sick. Null Hypothesis and Statistical Significance In practice, the difference between a false positive and a false negative is usually not so clear-cut. Since the tests are most often quantitively rather than qualitatively based, the results tend to be expressed in a confidence interval value less than 100%, rather than a simple Yes/No decision. This question of how likely the results are to be found if the null hypothesis is true is called statistical significance.

The Art of Winning Kaggle Competitions

 What is Kaggle? Learning data science can be overwhelming. Finding a community to share code, data, and ideas can se e m also seem like an overwhelming as well as farfetched task. But, there is one spot where all of these characteristics come together. That place is called Kaggle. Looked at more comprehensively, Kaggle is an online community for data scientists that offers machine learning competitions, datasets, notebooks, access to training accelerators, and education. Anthony Goldbloom (CEO) and Ben Hamner (CTO) founded Kaggle in 2010, and Google acquired the company in 2017. Kaggle competitions have improved the state of the machine learning art in several areas. One is mapping dark matter; another is HIV/AIDS research. Looking at the winners of Kaggle competitions, you’ll see lots of XGBoost models, some Random Forest models, and a few deep neural networks. The Winning Recipe of Kaggle Competitions involves the following steps: Step one   is to start by reading the competition gu