Data Types in C# and other languages

The following reference table compares commonly used C# numeric, scientific computing, and AI-oriented data types with their equivalents in Java, JavaScript, and Python. It also highlights memory consumption, binary representation, implicit conversion behavior, precision, and support for special values such as positive and negative infinity.

Quick Observation:
Traditional integer types (short, int, long) provide exact arithmetic but cannot represent infinity. Floating-point types (float and double) follow IEEE-754 standards and support ±Infinity. For financial calculations requiring exact decimal precision, C# provides the decimal type.

C# Data Type	Bytes Required	Internally Represented as Binary?	Implicitly Converted?	Supports ±Infinity?	Range (Approx.)	Precision	Java Equivalent	JavaScript Equivalent	Python Equivalent
short	2 bytes	Yes (Two's Complement)	Yes	No	~10⁴ (-32,768 to 32,767)	Exact Integer	short	Number	int
ushort	2 bytes	Yes (Unsigned Binary)	Yes	No	~10⁴ (0 to 65,535)	Exact Integer	No Direct Equivalent	Uint16Array	int
int	4 bytes	Yes (Two's Complement)	Yes	No	~10⁹	Exact Integer	int	Number	int
long	8 bytes	Yes (Two's Complement)	Yes	No	~10¹⁸	Exact Integer	long	BigInt	int
BigInteger	Dynamic	Yes	No	No	Limited only by RAM	Exact Integer	java.math.BigInteger	BigInt	int
float	4 bytes	IEEE-754	Yes → double	Yes	10⁻⁴⁵ to 10³⁸	6–9 digits	float	Float32Array	numpy.float32
double	8 bytes	IEEE-754	No	Yes	10⁻³²⁴ to 10³⁰⁸	15–17 digits	double	Number	float
decimal	16 bytes	Base-10 Representation	No	No	10⁻²⁸ to 10²⁸	28–29 digits	BigDecimal	Big.js / Decimal.js	decimal.Decimal
Vector3	12 bytes	Yes	No	Depends on float components	Float Range	6–9 digits	Vector3f	Object / Array	NumPy float32 vector
Vector<T>	16–64 bytes	Yes (SIMD Registers)	No	Depends on T	Depends on T	Depends on T	Java Vector API	Engine SIMD Optimizations	NumPy Arrays
Tensor<T>	Dynamic	Yes	No	Depends on T	Depends on T	Depends on T	DJL NDArray / TensorFlow Tensor	tf.Tensor	PyTorch Tensor / TensorFlow Tensor

AI & Machine Learning Note:
For traditional business applications, int, long, double, and decimal dominate usage. For scientific computing, graphics, machine learning, and neural networks, higher-level structures such as Vector3, Vector<T>, and Tensor<T> become significantly more important because they map efficiently to SIMD instructions, GPUs, and tensor-processing hardware.