Mantissa
A mantissa, more precisely called a significand, is the component of a scientific-notation or floating-point representation that carries a number’s significant digits or bits.
Statistics Every Programmer Needs
A mantissa is the fractional part of a common logarithm after its integer part has been separated.
- How it is used
- Tests whether numeric data conforms to Benford’s law beyond comparing leading-digit frequencies.
- How deep it goes
- Defines mantissas, derives them in Python, computes four statistics, and compares their distribution with uniformity.
- Examples
- Uses 287,437 and world population counts, with NumPy, pandas, SciPy, and rank-order plots.
- What you can do after
- Diagnose/debug with it by deriving mantissas and comparing their observed distribution with Benford’s uniform expectation.
Read how Statistics Every Programmer Needs explains mantissa →
Rust in Action: Systems programming concepts and techniques
A significand, also historically called a mantissa, is the significant-value component of a scientific-notation or floating-point representation.
- How it is used
- Identifies the significant-value field when decoding an f32 or constructing one from bits.
- How deep it goes
- Works through fractional-bit weights and a Rust decoding loop, while separating out exponent-dependent special cases.
- Examples
- Uses 42.42 as an f32 decoding example and a byte-to-f32 program as a bit-construction exercise.
- What you can do after
- Build it from scratch by decoding ordinary f32 mantissa bits or placing byte bits into a constructed f32.
Read how Rust in Action: Systems programming concepts and techniques explains mantissa →
100 Go Mistakes and How to Avoid Them
A mantissa is the base or significand component of a floating-point representation.
- How it is used
- Explains why Go floating-point calculations and comparisons can produce unexpected precision results.
- How deep it goes
- Defines mantissa and its bit allocation, then stops short of explaining the full conversion procedure.
- Examples
- Uses float32 multiplication of 1.0001 and a visual representation of its sign, exponent, and mantissa.
- What you can do after
- Evaluate floating-point results as approximations and choose error-based comparisons instead of exact equality.
Read how 100 Go Mistakes and How to Avoid Them explains mantissa →
Related concepts
- floating-point arithmeticpart of · Floating-point arithmetic is computation using finite-precision representations of real numbers, which can introduce rounding and range errors.
- logarithmpart of · A logarithm is the exponent to which a specified positive base other than one must be raised to produce a given positive number.
- sort_valuesused by · sort_values is a pandas method that returns data ordered by values in one or more columns or other labeled elements.
- exponentuses · An exponent is a number specifying the power to which a base or radix is raised, determining the scale of the resulting value.
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