Array types and conversions between types# NumPy supports a much greater variety of numerical types than Python does. This section shows which are available, and how to modify an array’s data-type.

Data type objects (dtype)# A data type object (an instance of numpy.dtype class) describes how the bytes in the fixed-size block of memory corresponding to an array item should be interpreted. It describes the following aspects of the data: Type of the data (integer, float, Python object, etc.) Size of the data (how many bytes is in e.g. the ...

This section shows which are available, and how to modify an array’s data-type. NumPy numerical types are instances of numpy.dtype (data-type) objects, each having unique characteristics. Once you have imported NumPy using import numpy as np you can create arrays with a specified dtype using the scalar types in the numpy top-level API, e.g ...

An array, any object exposing the array interface, an object whose __array__ method returns an array, or any (nested) sequence. If object is a scalar, a 0-dimensional array containing object is returned. dtype data-type, optional. The desired data-type for the array.

Cast the values contained in the array to a new data-type. ndarray.view. Create a view of the same data but a different data-type. numpy.dtype

NumPy is the fundamental library for array containers in the Python Scientific Computing stack. Many Python libraries, including SciPy, Pandas, and OpenCV, use NumPy ndarrays as the common format for data exchange, These libraries can …

Copy of the array, cast to a specified type. Parameters: dtype str or dtype. Typecode or data-type to which the array is cast. order {‘C’, ‘F’, ‘A’, ‘K’}, optional. Controls the memory layout order of the result.

Structured datatypes are implemented in numpy to have base type numpy.void by default, but it is possible to interpret other numpy types as structured types using the (base_dtype, dtype) form of dtype specification described in Data Type Objects.

The type of items in the array is specified by a separate data-type object (dtype), one of which is associated with each ndarray. As with other container objects in Python, the contents of an ndarray can be accessed and modified by indexing or slicing the array (using, for example, N integers), and via the methods and attributes of the ndarray .

An array object represents a multidimensional, homogeneous array of fixed-size items. An associated data-type object describes the format of each element in the array (its byte-order, how many bytes it occupies in memory, whether it is an integer, a …