Java Double Class

Last modified: April 13, 2025

The java.lang.Double class is a wrapper for the primitive double type, providing utility methods for working with double-precision floating-point numbers. It allows conversion between primitive double values and Double objects, enabling them to be used in collections, generics, and object-oriented programming contexts.

In addition to conversion functions, the Double class offers useful constants and methods for numerical operations. It includes functionalities for parsing, comparing, and checking special floating-point values such as NaN (Not-a-Number) and infinity. These features help ensure precision and correctness when handling floating-point arithmetic.

Double Class Methods

The Double class provides several static and instance methods for working with double values. Some key methods include:

parseDouble(String s) - Converts a string into a primitive
double, throwing a NumberFormatException for
invalid input.
valueOf(double d) - Returns a Double object
representing the specified primitive double value.
doubleValue() - Extracts the primitive double
value from a Double object.
compare(Double d1, Double d2) - Compares two
Double objects, returning a negative, zero, or positive result
based on their relative values.
isNaN(double d) - Determines whether the given
double value is NaN, which occurs in cases of
undefined mathematical operations.

By utilizing these methods, the Double class facilitates seamless conversions, comparisons, and numerical operations in Java, ensuring reliability when working with floating-point data.

Creating Double Objects

The Double class provides multiple ways to create instances representing double-precision floating-point numbers. Objects can be instantiated using the valueOf method, which is preferred due to its potential for caching frequently used values. Additionally, Java’s autoboxing mechanism automatically converts primitive double values into Double objects when necessary.

Main.java

package com.zetcode;

public class Main {

public static void main(String[] args) {

    Double d1 = Double.valueOf(3.14159);
    Double d2 = Double.valueOf("3.14159");

    // Using autoboxing
    Double d3 = 3.14159;

    System.out.println("d1: " + d1);
    System.out.println("d2: " + d2);
    System.out.println("d3: " + d3);

    // Converting back to primitive
    double primitive = d1;
    System.out.println("Primitive value: " + primitive);
}

}

This example demonstrates different approaches for creating Double objects. The valueOf method is often preferred because it may reuse existing instances instead of creating new ones. Autoboxing simplifies conversions, automatically wrapping primitive double values into Double objects when needed, reducing manual object creation.

Parsing Double Values

The parseDouble method converts a string to a primitive double. The valueOf method converts a string to a Double object. Both throw NumberFormatException for invalid input.

Main.java

package com.zetcode;

public class Main {

public static void main(String[] args) {

    String numStr1 = "3.14159";
    String numStr2 = "-123.456";
    String invalidStr = "3.14.159";
    
    // Parsing to primitive double
    double d1 = Double.parseDouble(numStr1);
    double d2 = Double.parseDouble(numStr2);
    
    // Parsing to Double object
    Double dObj1 = Double.valueOf(numStr1);
    Double dObj2 = Double.valueOf(numStr2);
    
    System.out.println("d1: " + d1);
    System.out.println("d2: " + d2);
    System.out.println("dObj1: " + dObj1);
    System.out.println("dObj2: " + dObj2);
    
    try {
        double invalid = Double.parseDouble(invalidStr);
    } catch (NumberFormatException e) {
        System.out.println("Invalid number format: " + invalidStr);
    }
}

}

This example shows how to parse strings into double values. The parseDouble returns a primitive, while valueOf returns a Double object. Both methods throw exceptions for malformed input.

Special Double Values

Special floating-point values arise from certain mathematical operations:

**NaN (Not-a-Number):** Represents an undefined result,
such as 0/0 or Infinity - Infinity. NaN values
propagate through calculations.
**Positive Infinity:** Occurs when a value exceeds the
largest possible double, such as 1.0 / 0.0.
**Negative Infinity:** Represents an infinitely small
value, such as -1.0 / 0.0.

These values follow specific rules in floating-point arithmetic. Operations involving NaN almost always result in NaN. Infinite values behave as expected in multiplication or addition but can become NaN when divided by another infinity.

By leveraging special values and their corresponding validation methods, developers can handle edge cases in floating-point computations effectively and prevent unexpected numerical errors.

Main.java

package com.zetcode;

public class Main {

public static void main(String[] args) {

    double nanValue = Double.NaN;
    double posInf = Double.POSITIVE_INFINITY;
    double negInf = Double.NEGATIVE_INFINITY;
    
    System.out.println("NaN: " + nanValue);
    System.out.println("Positive Infinity: " + posInf);
    System.out.println("Negative Infinity: " + negInf);
    
    System.out.println("Is NaN? " + Double.isNaN(nanValue));
    System.out.println("Is Infinity? " + Double.isInfinite(posInf));
    
    // Operations with special values
    System.out.println("NaN + 1: " + (nanValue + 1)); // NaN propagates
    System.out.println("Infinity * 2: " + (posInf * 2)); // Still infinity
    System.out.println("Infinity / Infinity: " + (posInf / posInf)); // Results in NaN
}

}

This example demonstrates how special floating-point values—NaN, POSITIVE_INFINITY, and NEGATIVE_INFINITY—behave in Java. It shows how to check for these values using isNaN and isInfinite and illustrates how they propagate through arithmetic operations, helping developers handle edge cases in numerical computations effectively

Comparing Double Values

Comparing double values requires special care due to floating-point precision errors. Direct equality checks using == may produce unexpected results when dealing with fractional values. The compare and compareTo methods provide reliable comparison mechanisms, correctly handling special values like NaN and infinity.

Floating-point precision limitations can lead to inaccurate equality checks. To ensure correct comparisons, follow these best practices:

**Use Double.compare(d1, d2):** This method
correctly handles special values such as NaN and infinity.
**Avoid direct equality checks (d1 == d2):**
Minor precision differences may cause inaccurate results.
**Use a tolerance for approximate equality:**
Math.abs(d1 - d2) < tolerance accounts for small
floating-point errors.
**Be cautious with NaN:** Any comparison
involving NaN returns unexpected results since NaN
is unordered.
Use BigDecimal for exact decimal
comparisons:
Unlike double, BigDecimal provides precise decimal
arithmetic, preventing rounding errors. Use
BigDecimal.compareTo for reliable equality checks.

For scenarios requiring precise decimal values, such as financial calculations, BigDecimal is the preferred choice. It avoids floating-point inaccuracies and allows control over scale and rounding behavior, ensuring correctness in numerical computations.

Main.java

package com.zetcode;

public class Main {

public static void main(String[] args) {

    Double d1 = 1.23456;
    Double d2 = 1.23457;
    Double d3 = Double.NaN;
    Double d4 = Double.POSITIVE_INFINITY;
    
    // Using compareTo method (instance method)
    System.out.println("d1 compareTo d2: " + d1.compareTo(d2));
    System.out.println("d3 compareTo d1: " + d3.compareTo(d1));
    System.out.println("d4 compareTo d1: " + d4.compareTo(d1));
    
    // Using static compare method
    System.out.println("Compare d1 and d2: " + Double.compare(d1, d2));

    // Equality comparison with tolerance
    double tolerance = 0.0001;
    boolean nearlyEqual = Math.abs(d1 - d2) < tolerance;
    System.out.println("d1 nearly equals d2: " + nearlyEqual);
}

}

This example demonstrates different approaches for comparing floating-point values in Java. It shows how the compareTo method correctly handles ordering, even with special values like NaN and infinity. The static Double.compare method provides another way to compare Double instances. Additionally, the example highlights the importance of using a tolerance value when checking for approximate equality, ensuring reliable comparisons despite minor floating-point precision errors

Converting Double Values

The Double class provides various methods to convert between double values and other primitive types. These methods allow for seamless data transformations, but it’s important to consider potential precision loss when converting floating-point numbers to integer types.

Common conversion methods include intValue, longValue, and floatValue. When converting to integer types, the fractional part is truncated rather than rounded, which may lead to differences in expected values.

When converting Double values, consider the following:

**Truncation vs. Rounding:** Converting a
double to an integer type removes the decimal part rather than
rounding.
**Precision Loss:** Converting to float may
introduce slight precision errors due to differences in storage format.
**Overflow Risks:** Converting large double
values to byte or short can lead to unexpected
overflow behavior.
**Hexadecimal Representation:** The
toHexString() method provides a base-16 floating-point format
that can be useful for debugging or storage.

Main.java

package com.zetcode;

public class Main {

public static void main(String[] args) {

    Double d = 123.456789;
    
    // Converting to other primitive types
    int intVal = d.intValue();  // Truncates decimal part
    long longVal = d.longValue();
    float floatVal = d.floatValue(); // Potential precision loss
    byte byteVal = d.byteValue(); // Risk of overflow for large values
    short shortVal = d.shortValue();
    
    System.out.println("Original double: " + d);
    System.out.println("intValue: " + intVal);
    System.out.println("longValue: " + longVal);
    System.out.println("floatValue: " + floatVal);
    System.out.println("byteValue: " + byteVal);
    System.out.println("shortValue: " + shortVal);
    
    // Converting to String
    String strVal = d.toString();
    String hexStr = Double.toHexString(d);
    
    System.out.println("toString: " + strVal);
    System.out.println("toHexString: " + hexStr);
}

}

This example demonstrates safe and efficient conversion methods, highlighting potential pitfalls developers should consider when working with floating-point data.

Double Constants and Limits

The Double class provides useful constants that represent the limits of double-precision floating-point numbers. These include MAX_VALUE, MIN_VALUE, and MAX_EXPONENT.

Main.java

package com.zetcode;

public class Main {

public static void main(String[] args) {

    System.out.println("MAX_VALUE: " + Double.MAX_VALUE);
    System.out.println("MIN_VALUE: " + Double.MIN_VALUE);
    System.out.println("MIN_NORMAL: " + Double.MIN_NORMAL);
    System.out.println("MAX_EXPONENT: " + Double.MAX_EXPONENT);
    System.out.println("MIN_EXPONENT: " + Double.MIN_EXPONENT);
    System.out.println("SIZE: " + Double.SIZE + " bits");
    System.out.println("BYTES: " + Double.BYTES + " bytes");
    
    // Demonstrating overflow
    double max = Double.MAX_VALUE;
    System.out.println("MAX_VALUE * 2: " + (max * 2));
    
    // Demonstrating underflow
    double min = Double.MIN_VALUE;
    System.out.println("MIN_VALUE / 2: " + (min / 2));
}

}

This example displays the limits of double-precision floating-point numbers. MAX_VALUE is the largest finite positive value, while MIN_VALUE is the smallest positive nonzero value. Overflow results in infinity, while underflow can result in zero.

Source

Java Double Class Documentation

In this article, we’ve covered the essential methods and features of the Java Double class. Understanding these concepts is crucial for working with floating-point numbers in Java applications.

Author

My name is Jan Bodnar, and I am a dedicated programmer with many years of experience in the field. I began writing programming articles in 2007 and have since authored over 1,400 articles and eight e-books. With more than eight years of teaching experience, I am committed to sharing my knowledge and helping others master programming concepts.

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