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

Learn about the IEEE 754 standard for 64-bit binary floating-point numbers, also known as double or float64. See the bit layout, exponent encoding, and range of values for this format.

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

Learn how to create and manipulate arrays of different numerical types in NumPy, including numpy.float64, a 64-bit floating point data type. See examples, conversions, and comparisons with other types.

https://www.slingacademy.com › article › understanding-numpy-float64-type-5-examples

Learn how to use numpy.float64, a data type for double-precision floating-point numbers, in NumPy and Python. See how it differs from standard float type, how to perform basic and array operations, and how to interoperate with other types.

https://note.nkmk.me › en › python-sys-float-info-max-min

This article explains how to get and check the range (maximum and minimum values) that float can represent in Python. In many environments, the representable range for float is as follows. e+XXX means 10 to the power of XXX. -1.7976931348623157e+308 <= f <= 1.7976931348623157e+308. Contents.

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

Learn how to store and use decimals in Julia with floating point numbers. Compare the features and limitations of Float16, Float32 and Float64 data types.

https://en.cppreference.com › w › cpp › types › floating-point

Unlike the fixed width integer types, which may be aliases to standard integer types, the fixed width floating-point types must be aliases to extended floating-point types (not float / double / longdouble).

https://numpy.org › doc › 1.20 › user › basics.types.html

Learn about the numerical types supported by NumPy, including numpy.float64, which is a double precision float with 64 bits. See how to create, convert and use arrays of different types, and how to access their properties and methods.

https://jakevdp.github.io › PythonDataScienceHandbook › 02.01-understanding-data-types.html

Understanding Data Types in Python. < Introduction to NumPy | Contents | The Basics of NumPy Arrays >. Effective data-driven science and computation requires understanding how data is stored and manipulated. This section outlines and contrasts how arrays of data are handled in the Python language itself, and how NumPy improves on this.