Hits: 43

# Java Program to Count Number of Digits in an Integer

## Example 1: Count Number of Digits in an Integer using while loop

``````
public class NumberDigits{

public static void main(String[] args){

int count = 0, num = 3452;

while(num != 0)
{
// num = num/10
num /= 10;
++count;
}

System.out.println("Number of digits: " + count);
}
}``````

Output

`Number of digits: 4`

In this program, `while` the loop is iterated until the test expression `num != 0` is evaluated to 0 (false).

• After the first iteration, num will be divided by 10 and its value will be 345. Then, the count is incremented to 1.
• After the second iteration, the value of num will be 34 and the count is incremented to 2.
• After the third iteration, the value of num will be 3 and the count is incremented to 3.
• After the fourth iteration, the value of num will be 0 and the count is incremented to 4.
• Then the test expression is evaluated to false and the loop terminates.

## Example 2: Count Number of Digits in an Integer using for loop

``````
public class NumberDigits{

public static void main(String[] args){

int count = 0, num = 123456;

for(; num != 0; num/=10, ++count) {
}

System.out.println("Number of digits: " + count);
}
}``````

Output

`Number of digits: 6`

In this program, instead of using a while loop, we use a for loop without any body.

On each iteration, the value of num is divided by 10 and count is incremented by 1.

The `for` loop exits when `num != 0` is false, i.e. num = 0.

Since, `for` loop doesn’t have a body, you can change it to a single statement in Java as such:

`for(; num != 0; num/=10, ++count);`

# Special 95% discount

## 2000+ Applied Machine Learning & Data Science Recipes

### Portfolio Projects for Aspiring Data Scientists: Tabular Text & Image Data Analytics as well as Time Series Forecasting in Python & R ## Two Machine Learning Fields

There are two sides to machine learning:

• Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
• Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

Data Science Resources: Data Science Recipes and Applied Machine Learning Recipes

Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!

Latest end-to-end Learn by Coding Recipes in Project-Based Learning:

Applied Statistics with R for Beginners and Business Professionals

Data Science and Machine Learning Projects in Python: Tabular Data Analytics

Data Science and Machine Learning Projects in R: Tabular Data Analytics

Python Machine Learning & Data Science Recipes: Learn by Coding

R Machine Learning & Data Science Recipes: Learn by Coding

Comparing Different Machine Learning Algorithms in Python for Classification (FREE)

`Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.  `