https://stackoverflow.com › questions › 43440821

float32 is a 32 bit number - float64 uses 64 bits. That means that float64’s take up twice as much memory - and doing operations on them may be a lot slower in some machine architectures. However, float64’s can represent numbers much more accurately than 32 bit floats.

https://en.wikipedia.org › wiki › Single-precision_floating-point_format

Single-precision floating-point format (sometimes called FP32 or float32) is a computer number format, usually occupying 32 bits in computer memory; it represents a wide dynamic range of numeric values by using a floating radix point.

https://softwareengineering.stackexchange.com › questions › 412050

Put it simply, 32 bit representation provides at least 7 digit of precision after decimal and double 64 bit provides close to 16 digits of precision after decimal. The offered precision is way more than our expectation (0.001).

https://en.wikipedia.org › wiki › Double-precision_floating-point_format

Double-precision floating-point format (sometimes called FP64 or float64) is a floating-point number format, usually occupying 64 bits in computer memory; it represents a wide dynamic range of numeric values by using a floating radix point.

https://numpy.org › doc › stable › user › basics.types.html

The bitsize is the number of bits that are needed to represent a single value in memory. For example, numpy.float64 is a 64 bit floating point data type. Some types, such as numpy.int_ and numpy.intp, have differing bitsizes, dependent on the platforms (e.g. 32-bit vs. 64-bit CPU architectures).

https://math.libretexts.org › Bookshelves › Scientific_Computing_Simulations_and_Modeling...

Julia has 16-,32- and 64-bit floating point numbers called Float16, Float32 and Float64 and by default on most systems is the Float64. There are two limitations to any floating-point number. First, the number of digits stored in the number and secondly, the maximum and minimum values.

https://newsletter.theaiedge.io › p › float32-vs-float16-vs-bfloat16

Float 32 can range between -3.4e^38 and 3.4e^38, the range of Float16 is between -6.55e^4 and 6.55e^4 (so a much smaller range!), and BFloat has the same range as Float32. To convert from Float32 to Float16, you just need to remove the digits that cannot fit in the 5 and 10 bits allocated for the exponent and the Mantissa.

https://clickhouse.com › docs › en › sql-reference › data-types › float

Float types in ClickHouse have the following aliases: Float32 — FLOAT, REAL, SINGLE. Float64 — DOUBLE, DOUBLE PRECISION. When creating tables, numeric parameters for floating point numbers can be set (e.g. FLOAT(12), FLOAT(15, 22), DOUBLE(12), DOUBLE(4, 18)), but ClickHouse ignores them.

https://docs.scipy.org › doc › numpy-1.13.0 › user › basics.types.html

Python’s floating-point numbers are usually 64-bit floating-point numbers, nearly equivalent to np.float64. In some unusual situations it may be useful to use floating-point numbers with more precision.