Fundamental Data Types − Float, Double & Long Double

Summary

In this video, the speaker discusses the differences between the float, double, and long double data types in programming, emphasizing their sizes, precision, and use cases. The video covers how these types are used to represent real numbers and contrasts floating point vs fixed point representations. The speaker demonstrates the impact of precision on real numbers with examples and shows how to print values accurately with specific format specifiers. Through coding examples, viewers learn about precision differences between the types, and how using the right data type can optimize both space and accuracy for scientific applications.

Takeaways

• 😀 Float, double, and long double are data types used to represent fractional or real numbers, like 3.14 or -3276.789.
• 😀 The size of these data types varies depending on the system. For example, float takes 4 bytes, double takes 8 bytes, and long double takes 12 bytes in the provided system.
• 😀 Float follows the IEEE754 Single Precision Floating Point representation, double follows IEEE754 Double Precision Floating Point, and long double follows Extended Precision Floating Point.
• 😀 Fixed point representation is a natural way to represent fractional numbers, but it has limited precision and a smaller range than floating point representation.
• 😀 Floating point representation allows for a much larger range of fractional numbers and is more flexible, as the decimal point 'floats' and can shift.
• 😀 Floating point is preferred over fixed point because it can represent a much wider range of values with greater precision.
• 😀 The need for three different data types (float, double, long double) is due to the varying levels of precision they provide for different applications.
• 😀 Float offers precision up to 7 digits, double offers precision up to 16 digits, and long double offers precision up to 19 digits.
• 😀 For scientific applications that require high precision, double or long double are preferred, while float is used when less precision is needed to save memory.
• 😀 When performing division between two integers, the result can be truncated unless the operands are converted to float or double for accurate fractional representation.

Q & A

What is the primary purpose of using the data types float, double, and long double?

These data types are used to represent fractional or real numbers, similar to how the 'int' data type is used to represent integers and 'char' for characters.

What are the sizes of the data types float, double, and long double in the example system?

In the example system, the size of 'float' is 4 bytes, 'double' is 8 bytes, and 'long double' is 12 bytes.

Why do the sizes of float, double, and long double vary across different systems?

The sizes of these data types depend on the architecture of the system being used, meaning that the sizes could be different on another computer.

What is the IEEE 754 standard, and how does it relate to float, double, and long double?

IEEE 754 is a standard for floating point arithmetic. Float uses the IEEE 754 Single Precision Floating Point representation, double uses IEEE 754 Double Precision, and long double follows Extended Precision Floating Point.

What is the difference between fixed-point and floating-point representation?

In fixed-point representation, the decimal point is fixed at a certain position, limiting the range of representable values, while in floating-point representation, the decimal point 'floats', allowing a much larger range of values.

Why is floating-point representation preferred over fixed-point representation in modern computers?

Floating-point representation allows for a larger range of values by shifting the decimal point, while fixed-point representation is limited and only allows precise representation of a small range of numbers.

How does the precision of float, double, and long double differ?

The precision of 'float' is up to 7 digits, 'double' can hold up to 16 digits, and 'long double' can store up to 19 digits.

What issue arises when performing division between two integers and storing the result in an integer variable?

The result is truncated because integer variables cannot store fractional values. For example, 4 divided by 9 would result in 0 instead of 0.44.

How can we avoid truncation when dividing two integers and store the result with fractional precision?

To avoid truncation, one of the operands should be converted to a floating-point number (e.g., 4.0 and 9.0) to ensure the result includes fractional precision.

What happens when you try to print a float, double, or long double with more decimal places than their precision can handle?

The value will be printed up to the limit of the data type’s precision. For example, 'float' will print up to 7 digits, 'double' will print up to 16 digits, and 'long double' will print up to 19 digits, truncating any extra digits.